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Posts Tagged 'Batman'
He's a Card
Posted in Film Reviews, tagged 2019, Batman, DC, drama, Joaquin Phoenix, Joker, Robert de Niro, superhero, Todd Phillips on October 5, 2019| Leave a Comment »
There are a number of ways one could approach the discussion of Todd Phillips' Joker. One of the best jokes in last year's Teen Titans Go! To the Movies concerned a succession of spoof Batman spin-offs desperately trying to wring every last drop of commercial potential out of the character's mythology – a movie about the Batmobile, a movie about Batman's utility belt, and so on – and from a certain point of view the new movie does look like exactly this sort of thing.
Or, one could suggest that the new film comes from the same place as recent successes like the Deadpool films and Venom: there does seem to be a market for dark, morally ambiguous fantasy films aimed at an older audience, and you don't get much darker or more morally compromised than the world's most famous supervillain. (If you wanted to be really nasty you could start comparing it to the 2004 Catwoman film, which it likewise bears a passing resemblance to, but that would surely qualify as unnecessary cruelty.)
Then again, you could also view it as the inevitable next step in the rise of comic book movies to complete world domination: superhero films routinely make billions, and are beginning to acquire a certain sort of respectability – Black Panther was nominated for Best Picture, and it's a reasonable bet that Avengers: Endgame will be, too – and Joker looks very much like a calculated attempt at a classy, serious film intent on receiving critical acclaim in addition to its almost-inevitable financial success.
Who knows? Maybe it's all of these things. What we can definitely say is that it is set in a squalid, 1980s version of Gotham City, where we find Arthur Fleck (Joaquin Phoenix). By day, he is a white-faced, green-wigged clown for hire; by night, an aspiring stand-up comedian (unexpectedly, pretty much the only joke we hear him deliver is a classic Bob Monkhouse line). He is a deeply troubled man twenty-four hours a day, though, living alone with his mother, obsessed with a TV chat show host and comedian (Robert De Niro), taking seven different medications for various psychiatric conditions, and afflicted with a curious nervous complaint causing him to laugh uncontrollably in stressful situations.
But, over the course of one hot summer, with the city wracked by a financial crisis, those stressful situations keep coming, taking their toll on Arthur's fragile mental state. The tipping point comes when he is attacked on the subway by three entitled, arrogant young employees of the Wayne corporation: in a matter of seconds his assailants are dead and he realises he feels much more cheerful and comfortable with himself. News reports of a killer clown preying on the wealthy are soon spreading, while it is becoming increasingly clear that a nihilistic force of chaos is incubating within Arthur, only waiting for the right moment to manifest itself…
It may be a coincidence, but films featuring the Joker have a tendency to attract controversy more or less in proportion to the acclaim received by the actor in the role: the 1989 Batman featured one of Jack Nicholson's biggest turns, and was a very rare example of a film which required the BBFC to create a new certification for it (the 12 rating, should you be wondering). Heath Ledger famously won a posthumous Oscar for his performance in The Dark Knight, but the film was again mired in controversy for supposedly glamorising knife violence. It should come as no surprise that Joker is also getting some commentators hot under the collar, the suggestion being that it may inspire copycats to perpetrate the same kind of violence that the Joker indulges in here.
There is certainly a question to be asked about what exactly is going on with a film like this, and it's the same one many people asked about the last movie to feature the Joker, 2016's Suicide Squad: why do a movie about the Joker without Batman in it? Isn't the whole point of the character that he's an antagonist and a foil to someone else? One of the many smart things about The Dark Knight was its handling of the unhealthily co-dependent relationship between the two of them. All the word on Joker is that this is a standalone film; any appearances of the character in the foreseeable future will feature the Jared Leto version, not Phoenix's. So what's the point of an origin film for a someone we're never going to see again?
Well, the quality of the film is more than high enough to answer most criticisms along these lines: the depiction of a grimy, seething Gotham is as good as any other we've seen in the movies, and the film is built around a characteristically intense and committed performance from Joaquin Phoenix. This is quite a long film, with the recognisable Joker persona not appearing until the closing stages of it, and Phoenix takes us through every step of Fleck's psychological disintegration and transformation. This is the kind of performance that normally gets award nominations when it isn't in a comic book movie; it will be interesting to see how hard the old prejudices die.
Phoenix works hard to be pitiable and relatively sympathetic early in the film, but by the climax the character has convincingly become a genuinely unsettling and frightening psychopath. The film obviously owes a big debt to The Dark Knight – in both films the Joker chooses to paint his face, rather than having his skin chemically bleached in an accident – but the climax is equally obviously inspired by a sequence from Frank Miller's The Dark Knight Returns (probably the single most influential Batman story of all time). It's Miller's version of the Joker which Phoenix seems to be channelling.
It's still the case that the film-makers have made up a new genesis for the Joker from scratch (the Joker's creators felt that giving him a history would humanise the character too much, something Christopher Nolan later agreed with) and so the decision to make the film about mental illness is a deliberate choice on their part. Again, one wonders whether this is a slightly portentous comic book movie which has adopted some very mature subject matter in order acquire some spurious gravitas, or if it's a seriously-intentioned drama about the corrosive effects of urban alienation and isolation that's roped in some of the Batman characters to make itself more commercial. I'm really not sure; the answer may actually lie in the film's various homages to films made around the time it is set – most obviously King of Comedy and Taxi Driver, of course, but there are also surely references to Network and The French Connection.
All the call-backs are respectful and clearly sincere, but they seem to be the main reason why the film is set decades in the past. This is another decision which does have awkward consequences, especially when you consider that Joker seems to want to comment on various current social issues – for instance, the Joker finds himself adopted as the figurehead for an Occupy-style anti-capitalist movement (in line with this, the film features an atypically unsympathetic take on Thomas Wayne (played by Brett Cullen)). None of this feels especially thought-through, though, and the film doesn't feel like it's presenting a cohesive thesis. Heath Ledger's enigmatic Joker was an agent of chaos and madness, demanding the other characters in the film re-assess their attitudes and moral choices; Phoenix's more accessible Joker is just a symbol of chaos and madness, the film too introspective for him to be anything more.
Then again, in the absence of Batman, he doesn't really need to be. I suspect that this is a film which is liable to be over-praised for the way it brings a grim, gritty, psychologically naturalistic approach to its comic book source material (ironically, the writers of comic books figured out that going dark and mature was essentially a blind alley over two decades ago). The film is impressively made and Phoenix, as noted, gives a brilliant performance, but it offers little in the way of genuine insight and it runs the genuine risk of taking itself too seriously. Without Batman or an equivalent figure to engage with, the Joker isn't an especially interesting or significant character. Todd Phillips and Joaquin Phoenix are to be commended for making a film which to some extent manages to avoid confronting this problem, but this doesn't mean they've solved it. Joker is very impressive on its own terms, it's just that those terms are undeniably odd.
To Live and Die in Gotham City
Posted in Film Reviews, tagged 2012, Anne Hathaway, Batman, Christian Bale, Christopher Nolan, DC, Gary Oldman, Michael Caine, superhero, The Dark Knight Rises, Tom Hardy on July 21, 2012| Leave a Comment »
There are keenly-anticipated films, and then there are films with a genuine buzz around them, and then there are films people are desperately excited to see. And then there's The Dark Knight Rises.
The first breathlessly agitated articles about Christopher Nolan's final Batman movie started appearing nearly eighteen months ago – I should know, I wrote one of them myself. Even four months ago, respectable magazines were writing articles on the movie discussing the serious issue that some people were worried the antagonist's dialogue might be completely unintelligible. Even in a perfect world, this film would still have received virtually blanket media coverage on its opening weekend.
This, of course, is usually a recipe for crushing disappointment, as many people who went to one of the midnight showings of Prometheus would happily tell you (and, judging from what I've seen, would do so at great length). Nevertheless, some kind of minor miracle has been achieved, because The Dark Knight Rises is… satisfying. I know that sounds like damnation by the faintest of praise, but it really isn't. Thinking about this film the word I come back to time and time again is 'satisfying', and I think this is not something to be underestimated.
Ten years ago, a mysterious organisation calling itself the League of Shadows attempted to recruit vengeance-hungry orphaned billionaire Bruce Wayne (Christian Bale) to become one of its elite assassins. Wayne broke away from the League and transformed himself into the masked vigilante and defender of Gotham City, Batman, killing his former mentor.
Eight years ago, Batman's attempts to save Gotham were critically imperilled when the city's heroic DA was driven mad and went on a killing spree before ultimately dying. To protect the dead man's reputation and his work, Batman framed himself for the man's actions.
No one has seen Batman since that night, and Bruce Wayne has become a crippled, embittered recluse. But Gotham is, it seems, a much more hopeful city. Dark forces are gathering, however – morally-ambiguous jewel thief Selina Kyle (Anne Hathaway) is amongst the least of them, but quick to catch Wayne's attention. Much, much more of a threat is the masked mercenary Bane (Tom Hardy), another former member of the League of Shadows, who's in town pursuing a machiavellian scheme of his own. Even if Batman returns to confront Bane, does he still have the ability to defeat him? And is Bane simply just following orders in expectation of getting a paycheck…?
One can understand the reluctance of Christopher Nolan and his team to make this final return to the world of Batman, given their massive achievements with Batman Begins and The Dark Knight, and the less-than-stellar record of many third instalments in series. Just making a good movie would have been a significant success; making one as good as this is a stellar achievement.
The Dark Knight Rises has the same virtues as Nolan's other movies: effortless technical grace and polish, a very intelligent script, strength in depth in the cast list, and the overall sense that while this may be a major studio production, that doesn't mean the film has to assume the audience is composed entirely of morons. One has to commend DC for giving Nolan the latitude to make the film he wanted to make (completely free of the stereoscopic scourge, as well), even if this means letting him do some slightly surprising things to the characters and setting.
This is not to say that Nolan cuts loose entirely from the existing Batman mythology, as many characters from the previous movies return, and a number of iconic scenes from the most famous Batman comics are brought – here it comes again – very satisfyingly to the screen. Impressively, he even manages to largely rehabilitate Catwoman, following the number done upon her reputation by Pitof and Halle Berry – although Anne Hathaway doesn't have quite the obvious intelligence or wit to completely nail the character.
The film's powerhouse performance and most memorable creation is, however, Tom Hardy's Bane. Following a possibly-dodgy start to his career playing the Picard clone in Nemesis, Hardy has been steadily popping up in recent films, always threatening to give a magnetic, movie-stealing performance. Here I would say that finally happens. Hardy's physical presence is imposing, but his vocal performance is even more remarkable, giving the character an almost-Shakespearean delivery without making him feel corny or hammy. I'd say there's quite a big difference between the comics Bane and Nolan and Hardy's version, but if anything the film-makers have improved on the original this time.
(As to whether there are any surprise appearances in this film from other notable Batman villains, either from the earlier movies or new to this one – well, the film-makers have decided to keep quiet about this, which seems to me to be an eminently sensible plan and an example I will be following.)
I could spend quite a long time going through all the things which are great about this movie, even just the performances: Michael Caine as Alfred, Joseph Gordon-Levitt as an idealistic young cop, Tom Conti in a cameo role I'd better not spoil. Hans Zimmer's score is rousing stuff, if perhaps a bit too fortissimo in places: some of the dialogue gets a little drowned out. Nolan also feels much more comfortable integrating comic relief into the story, something which occasionally felt a bit awkward in the past.
One of the most striking elements of this film which I do feel deserves a fuller mention is the level of its social commentary. All of the Nolan Batman films have had interesting things to say about the difference between law and justice and the real consequences of someone like Batman operating, but there's a long sequence towards the end of this film which seems to me to be saying very sharp and unusual things about current politics and economics. Throughout the film the people in the firing line are stockbrokers and bankers and businessmen, who are nevertheless not presented tremendously sympathetically. (This is clearly a film fully aware of the economic realities of life in 2012 and how this has shaped people's attitudes.) Bane's organisation basically presents itself as the militant wing of the Occupy movement, intent on bringing about some degree of social justice and redistribution of wealth – but, as this is Bane's organisation, we know that they are in the wrong. Even the 'morally flexible' Catwoman realises this. And yet the film refuses to offer easy answers or pat solutions: it's mature enough to suggest, as these films always have, that the world is a complex place which does not lend itself to such things.
Lots of stuff blows up, too, of course, orchestrated with Nolan's customary verve. Perhaps the great achievement of this series has been the way in which it has blended intelligent themes and characterisation with the demands of a blockbuster superhero movie (I notice a cliche developing: the hunt for a clean, renewable energy source is a crucial plot point here, as it was in The Avengers, and a couple of movies prior to that – and, while we're on the subject, watching The Dark Knight Rises back to back with The World is Not Enough might prove an illuminating experience in some respects).
I imagine one of the pleasures to be had when returning to this film will be to admire Nolan's legerdemain in setting up the conclusion. All the elements are there, in plain view (sometimes jarringly so), and yet come the end of the film he manages to arrange them in a manner which is both ingenious, quite moving, and – yet again – very satisfying as a genuine end to the story (suffice to say, Batman does something he's never done before). The real trick is that the film presents something which is very definitely a proper ending, but still makes you want to revisit this world and see what happens next to the characters: but it would be brave of a director to attempt to follow in Nolan's footsteps so closely, and brave of DC to let them try.
However, however, however. The Dark Knight Rises is cleverly written, strongly acted, flawlessly realised and directed with indisputable virtuosity – but for all of this it is still quite a difficult film to honestly connect with – for all of its many satisfactions, the overall impression I got was of a vast, intricate, high-powered, precision-tooled machine: a phenomenal piece of engineering but quite hard to engage with emotionally for an extended period of time.
I suppose this has been true to some degree of all the Christopher Nolan films that I've seen – perhaps the sheer scope of his imagination and ambition prevents them from having a genuine human centre. Even so, Christopher Nolan is still arguably the most exciting director working in the world today, and if he finds a way to invest his movies with authentic heart a place amongst the all-time greats is his to be had. The Dark Knight Rises is proof enough of that – Nolan has made a terrific film on so many levels, and one which deserves to be remembered as that and that alone.
The Crying-on-the-Inside Kind of Clown
Posted in Film Reviews, tagged 2000s, Aaron Eckhart, Batman, Christian Bale, Christopher Nolan, DC, drama, Gary Oldman, Heath Ledger, Maggie Gyllenhaal, Michael Caine, superhero, The Dark Knight on May 21, 2011| Leave a Comment »
From the Hootoo archive. Originally published July 31st 2008:
And finally, just when you thought you could get through an entire column without one of those movies showing up… yes, it's Christopher Nolan's The Dark Knight, which has finally rumbled into public view trailing the kind of rapturous notices most producers would happily cut off a limb to receive – and I'm not inclined to disagree with the consensus on this occasion.
For those of you recently returned from a holiday on Neptune, this is another tale of goings-on in Gotham City. The crusade against crime launched by the Batman (an apparently laryngitic Christian Bale) and Lt Gordon (Gary Oldman) seems to be bearing fruit, in the form of the city's new fiercely idealistic and dedicated District Attorney Harvey Dent (Aaron Eckhart) – even if he is dating Batman's old girlfriend Rachel (Maggie Gyllenhaal). However, the city is about to be plunged into a nightmare as Batman's continuing harassment of the mob forces them to accept the assistance of a demented psychopathic genius calling himself the Joker…
Heath Ledger's performance as the Joker has, for obvious reasons, attracted a lot of attention – but one would hope that this would have been the case anyway, as he is utterly mesmerising. The Joker is hilarious and terrifying at the same time: he does a piece of business with a pencil that left the audience I saw this movie with trying to gasp, groan, and laugh at the same time, while later on there's a scene where he wanders out of an exploding building in (comically unconvincing) drag that's simply jawdropping in its audacity and confidence. This is the first screen version of the character who can credibly take on Batman in a physical confrontation, something Nolan fully exploits. Even more impressively, Ledger manages all this without seeming obviously hammy or over-the-top like some Nicholsons – sorry, I meant to say actors – who have played the part in the past. He's aided by a script which allows the character a chance to actually develop in the course of the movie, progressing from a (relatively) simple insane killer to the more complex Joker of recent comics.
But, surprisingly, he isn't allowed to dominate the film – although he does rather eclipse the movie's other classic villains, who either make cameos (a blink-and-you'll-miss-him appearance from Cillian Murphy as the Scarecrow) or show up rather near the end. Eckhart gives an intelligent and plausible performance as Dent, and it's a bit of a shame he doesn't get more room to display all the facets of the character. The biggest miracle of all is that Christian Bale, who as Batman doesn't get to properly use his voice or most of his face, isn't reduced to an onlooking cipher as happened in the 90s Bat-movies, although his performance is necessarily understated. Michael Caine and Morgan Freeman show up from the last movie as well, and give neat demonstrations of how to steal scenes from the younger actors.
The technical virtuosity of Christopher Nolan's direction shouldn't really have surprised me as much as it did, but this is probably simply because he gets Batman right in a way other directors have never managed. For example, rather than being merely a menacing icon waddling around in inch-thick rubber, here Batman is a convincingly agile and skilled martial artist. Nolan also opens the movie out to a global scale, giving his hero a brief but typically energetic encounter with the Hong Kong Triads on their home turf. There seemed to me to be a bit less reliance on Bat-gadgets than usual, too, with the obvious exception of the new Batpod – which looks undeniably cool but struck me as rather silly in both name and concept. Such is Nolan's command of the medium that, for a few shocking minutes, he even had me believing that he'd been allowed to permanently and properly kill off one of the central Batman characters. The only real weakness in Nolan's direction, in fact, is his slight awkwardness when it comes to comic relief: Caine and Freeman have no problems delivering their one-liners but elsewhere his editing is a bit too staccato.
This is a piddling little criticism considering the colossal level of crash-bang-wallop the movie delivers, especially when coupled to its interest in the deeper morality of the issues involved. This finds its most obvious articulation when the film repeatedly asks how a principled man can hope to counter one wholly without moral compass, and intersects rather neatly with a meditation on how one can repeatedly confront evil without becoming contaminated by it (one would have expected this Nietzschean line of thought to turn up in a Superman movie, but never mind). Implicit in the film is the notion that it's the mere existence of Batman himself that has conjured all the maniacs he must battle into existence, and that all the death and destruction which occurs is ultimately his fault. On this level, The Dark Knight isn't an especially cheerful movie: its view of human nature for most of its running time is so relentlessly bleak that when it does attempt to offer a ray of hope it almost doesn't ring true.
So, yes: we have a new and very strong candidate for the title of best superhero movie ever (not that this isn't much more than just a superhero movie). One is obliged to wonder just how on Earth Nolan and company can possibly top this one (not least because most of the classic Batman villains aren't really usable for various reasons – my money's on the Riddler showing up next time, though), but they've already repeatedly demonstrated that no-one else is better qualified to try. Highly recommended.
King Jack Joker and the Triumph of Style
Posted in Film Reviews, tagged 1980s, Batman, DC, Jack Nicholson, Michael Keaton, superhero, Tim Burton on February 6, 2011| Leave a Comment »
It must have been nearly four years ago, and as usual I was boring regaling a colleague with news about upcoming film releases. He turned out to be completely ignorant of the then-around-a-year-off release of The Dark Knight, and utterly aghast when I revealed that Heath Ledger was playing the Joker.
'You can't have Heath Ledger as the Joker!' he cried. 'Jack Nicholson's the Joker! Nobody else can play that part!'
Less than four years ago, like I say: how things can change, huh? (And to be fair I was very dubious about the wisdom of casting Ledger myself for quite a while.) But it does show what a massive pop-cultural presence the 1989 Batman movie remained for quite a long time. Certainly it was inescapable that summer, almost omnipresent on TV, radio, and in terms of merchandise. (A friend of mine constructed his own batarang in metalwork class, with which he then proceeded to give himself a spectacular black eye – and unless he gets in touch with my people regarding a financial settlement, I will find it hard to find a reason not to reveal his identity. Hello, Steve. Hope you're well.)
Christopher Nolan's Batman films have reaped such deserved popular and critical success that the four movies that came out between 1989 and 1997 seem to have been largely forgotten about. And a good thing too, you might say, given the embarrassing excesses and general incoherence the series was prone to for much of that time. Well, maybe – but watching the original Tim Burton film again for the first time in ages, I can't help feel this is a film that doesn't really deserve it.
You could make a good case for arguing that Batman is the first modern superhero movie, in that it genuinely attempts to bring the essence of the comic-book to the screen. (The Christopher Reeve Superman movies are terrific – the first couple, anyway – but don't bear much resemblence to the book in terms of their tone and plots.) The plot is certainly archetypal stuff – masked hero makes his debut, shortly followed by a grotesque villain of some kind, and the two of them battle it out in a succession of big set-pieces. And indeed much of the script just seems like the result of an exercise in ticking boxes and hitting marks.
However, the memorable stuff in this movie isn't in the script, anyway – not that the screenplay is entirely mechanical, neatly undercutting the audience's expectations from the very beginning (what looks like it's going to be Batman's origin turns out to be something slightly different). As a director of motion pictures, Tim Burton's always seemed more interested in pictures than movement, and the visual style of this movie is rather more impressive than its action choreography. The look of the film – Gotham City seems to exist in some odd time-warp, stranded between the 40s and the 80s – may not be especially coherent, but at the time it was groundbreaking: such overt art-direction of a film with an ostensibly present-day setting had never really happened in a blockbuster before.
And so where Nolan created a realistic Batman who could plausibly exist in the real world, Burton creates an unrealistic world in which a fantasy figure like Batman seems entirely at home. I think this is a considerable achievement, and not something to be dismissed out of hand. You can see the excesses that would come to define the series lying in wait, of course, as first Burton and then Joel Schumacher chose to frame the later films solely in terms of their visuals, but the plot and script here are strong enough to support the visuals.
Acting-wise – oh, well, let's face it, we're talking about Nicholson. All the work, excellent, good, indifferent and poor, turned in by Michael Keaton (second-billed, tellingly), Kim Basinger, Jack Palance, Robert Wuhl, Michael Gough and every other performer – all of it is utterly obliterated by a giant, ravenous performance by an off-the-leash Jack Nicholson. Nuanced and understated it isn't – neither, to be perfectly honest, is it especially sinister – but on this occasion it really sort of works, simply because it means your eyes are magnetically drawn to something other than the art direction.
On another level, it's interesting to compare Burton's Batman with Nolan's The Dark Knight, simply because they both owe an obvious debt to Frank Miller's The Dark Knight Returns, which came out in 1986. Miller's book brought an iconic grandeur to Batman and Gotham City, as well as unprecedented moral and psychological complexity – and while Burton chose to concentrate on the former elements, Nolan has opted for the latter.
At the moment, of course, it's Christopher Nolan's approach which is in fashion, with Burton's style somewhat out of favour. However, it seems highly unlikely that people are going to stop making or going to see Batman movies after Nolan moves on, and it's quite possible things may swing back the other way, or achieve some sort of fusion between realism and fantasy. If that leads, in passing, to a reappraisal of the 1989 Batman, then all the better.
Coming soon to a Gotham near you…
Posted in Film Waffle, Geeky meltdown, tagged Bane, Batman, Catwoman, Christopher Nolan, DC, superhero, The Dark Knight Rises on January 20, 2011| Leave a Comment »
And so t'internet explodes as Christopher Nolan reveals who the bad guys will be in next year's most anticipated film, The Dark Knight Rises (Chris, if you're reading this – at least think about changing the name). Nolan is clearly a guy who relishes a challenge: not content with trying to better a film which was a deservedly massive popular and critical success, not content with trying to beat the final-chapter-of-a-trilogy jinx, he's also decided to do so while attempting to rehabilitate characters who were fairly comprehensively slimed the last time they showed up on the big screen.
Ever since the opening weekend of The Dark Knight there has been avid (one might even say fervid) speculation as to who Batman's opponents were going to be in the final film. The Riddler was a popular choice for a long time, although a fake leaked screenplay recently led some people to suspect Nolan was going for a bunch of rather more obscure characters – namely Professor Strange, Talia al'Ghul, Black Mask and Killer Croc. All of which turns out to be complete hoo-hah. The winners of the Who Gets To Be In This Movie contest are… (drum roll) Catwoman and Bane.
On one level it shouldn't be any surprise that Catwoman (to be played by Anne Hathaway) has made it into the Nolan series, as she's in the premier league of Batman characters – created by Bob Kane in 1940 for the first issue of Batman's own book, she was in the TV show (my favourite was Julie Newmar, other opinions are equally valid), and Batman Returns, and supported 170+ issues of her own book. Basically, nearly everyone knows who she is, and she's not completely weird – so she's a good fit for one of Nolan's street-level blockbusters.
Try not to think about this sort of thing. Yes, I know it's difficult.
On the other hand, two words: Halle Berry. When I first wrote on this topic my opinion was that Catwoman was 'unusable', simply because of the toxic legacy of the 2004 Catwoman movie, which your correspondent reviewed at the time using words like 'ham-fisted', 'offensive' and 'depressing' (and I cut it some slack compared to a lot of people). That said, the Berry movie will be comparatively ancient history by the time TDKR comes out and if Nolan thinks he can restore Catwoman's credibility I'm happy to believe him.
Bane, on the other hand, is a relatively new and obscure character, first popping up in 1993 when he set about terrorising Gotham City, wearing down Batman both physically and psychologically, and then breaking his spine and putting him in a wheelchair (relax, readers, this is comics: he got better eventually). In the comics, Bane's wont to wear a slightly garish costume that makes him look like a Mexican wrestler (you can bet that Tom Hardy won't be so attired in the movie), but the character has a lot going for him – extremely physically formidable (albeit with a major steroid problem), tactically brilliant and deeply perceptive. If they get Bane right in the new movie the results could be very exciting.
At least it's easier to stop yourself thinking about this sort of thing.
On the other hand, two words: Joel Schumacher. Although he wouldn't thank me for reminding people of this, Bane's already made it to the big screen, in the notorious piece of junk Batman and Robin (I still think it's better than Batman Forever, but that's just me). Bane's demoted to the position of being Poison Ivy's chief henchman in this film and is generally a grunting, shuffling travesty of his comics incarnation (though the costume is almost exactly the same). That said, being such a minor character, he escapes the worst of the indignities heaped upon all the leads and I expect most people won't realise that Jeep Swanson in the Schumacher movie and Tom Hardy in TDKR are actually meant to be playing the same character. Unless they see something like this blog post. Damn. Move along, everyone, forget what you've just read…
Anyway, at least now people can stop their endless speculations as to who the bad guys will be, and start their endless speculations about costume choices and script elements. I'm rather more interested to see what Nolan does with Bane than Catwoman, to be honest, but either way the final movie should be compelling stuff – not that we didn't know that already, of course.


Sedgefield Middle is a PBIS school. This means that we have a school-wide positive behavioral intervention system in place. Students are taught expected behaviors in school at the beginning of the year and throughout the year. These expected behaviors are described in our SFM PBIS Matrix attached below. Students are rewarded with Shark Points via our PBIS rewards app for exemplifying the expected behaviors. Each student can access their points online, and show you how many points they have earned. The points are used for in class incentives and attendance at our quarterly Shark Fests to celebrate reaching our quarterly Goals.


text:
Q: I want to add firebase into my flutter app but when i set it up, am having a lot of errors in VS Code i followed this tutorial on youtube https://www.youtube.com/watch?v=LnpGU8vj7TI in order to set firebase with flutter,and after doing exactly as the guy did am having a lot of errors showing in my terminal when I try to run the main.dart file

import 'package:firebase_core/firebase_core.dart';
import 'package:flutter/material.dart';
import 'package:flutter/widgets.dart';

void main() async{
  WidgetsFlutterBinding().ensureVisualUpdate();
  await Firebase.initializeApp();
  runApp(My_pp());
}

class My_pp extends StatefulWidget {
  @override
  State<StatefulWidget> createState() {
    // TODO: implement createState
    return My_ppstate();
  }
}

class My_ppstate extends State<My_pp> {

  Widget build(BuildContext context) {

    return MaterialApp(
      home: Scaffold(
        appBar: AppBar(
          title: Text('hello'),
          backgroundColor: Color.fromARGB(50, 255, 255, 5),
        ),

      ),
    );
  }
}

and the errors are

lib/main.dart: Warning: Interpreting this as package URI, 'package:led_blink/main.dart'.
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/animated_icons.dart:9:8: Error: Not found: 'dart:ui'
import 'dart:ui' as ui show Paint, Path, Canvas;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/animated_icons.dart:10:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/app.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' as ui;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/app_bar_theme.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/arc.dart:6:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/bottom_app_bar_theme.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/bottom_navigation_bar_theme.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/bottom_sheet.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/bottom_sheet_theme.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;
^
/D:/flutter_windows_2.0.3-stable/flutter/packages/flutter/lib/src/material/button_bar_theme.dart:5:8: Error: Not found: 'dart:ui'
import 'dart:ui' show lerpDouble;

this is the flutter doctor --verbose results:

the build.gradle from the android folder

the build.gradle from the app folder

A: (answer may not work in future)
(but it work for me now as of writing this answer in April 2021)
i have no idea why its happen but try chaining decencies
android/build.gradle (app level)
    classpath 'com.android.tools.build:gradle:3.5.4' //<---- change this
    classpath 'com.google.gms:google-services:4.3.4'

redpajama_set_name: RedPajamaStackExchange


text:
Castrotierra de Valmadrigal est une commune d'Espagne dans la province de León, communauté autonome de Castille-et-León. Elle s'étend sur  et comptait environ 126 habitants en 2011.

Commune dans la province de León

redpajama_set_name: RedPajamaWikipedia


text:
UncategorizedWFG News
WFG adds representative for New York, New Jersey area
By WFG NewsOctober 6, 2011 No Comments
WFG National Title Insurance Co. has added Manny Losquadro to grow and support its customer base in New York and New Jersey.
Losquadro joins WFG National Title with 15 years of experience in the settlement services industry. He most recently held a role in agency sales with one of the nation's largest title insurance companies, spending almost 10 years between two divisions in that position.
From 1996 to 2002, he served as agency auditor for the same national insurer. With WFG National Title, Losquadro will serve as an agency representative. He will work to build new agency relationships in New York and New Jersey, as well as serving as a resource for existing customers.
"Manny brings a thorough understanding of the title industry, and what it takes for an independent agency to thrive," said Joseph Drum, executive vice president for WFG National Title. "He also has a number of good relationships in the region and will be another strong partner to WFG National Title's east coast agencies."
Losquadro is a graduate of SUNY College at Old Westbury, where he received a bachelor's degree in finance.
Manny Losquadrosettlement servicestitle and escrowtitle industrytitle insuranceWFG National TitleWFG National Title CompanyWFG National Title InsuranceWFG TitleWilliston FinancialWilliston Financial Group
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redpajama_set_name: RedPajamaCommonCrawl


In 2007, about 150,000 to 200,000 children in the Katanga province of the DRC made their living through the informal and illegal activity of artisanal or small-scale mining (ASM). Over the past years, there has been a rise in artisanal mining which is mainly due to the country's under-development, lack of employment, corruption, and ineffective government. ASM activity is still unregulated and abuses are soaring, with artisanal miners being subject to excessive informal taxes. Despite the decline in mineral prices, ASM is still one of the few economic opportunities in the country and changes in the government's mining policy are becoming imperative.
In a copper mine near the town of Kambove in the south-eastern province of Katanga in the Democratic Republic of the Congo (DRC), one can see dozens of teenage boys with empty rice sacks on their backs, running to grab small pieces of copper forgotten in the debris and to escape rocks falling from an unloading truck up above.
These children are amongst the many thousands in Katanga who are forced to make a living through artisanal (or small-scale) mining. In 2007, an estimated 150,000 to 200,000 in Katanga province made their living through the informal and illegal activity, working in hazardous conditions for little pay.
This April, Katanga's Governor Moise Katumbi voiced his concern at the perceived increase of artisanal miners in the copper- and cobalt-wealthy region, but added that his administration can do little to stop illegal mining given that it is a national problem. Indeed, the provincial government has been short on making concerted efforts to tackle the long-standing issue.
Furthermore, while the current global economic crisis and the decline in mineral prices have made artisanal mining less viable, the alternatives in the region are scarce. Reports suggest that while some former miners have turned to agriculture, others have turned to other criminal activities.
There are two forms of mining in the DRC: formal large scale mining operations (LSM), and informal artisanal and small-scale mining activities (ASM). Artisanal mining can be dangerous and is seen by the state as an burden, but the low start-up costs make it attractive to those with no other options. Indeed, the rise of artisanal mining can be seen an indirect symptom of under-development, lack of employment, corruption, and ineffective government. Without viable formal alternatives, artisanal mining is one way to make a livelihood although local sources told Think Africa Press that miners seldom make more than $3 a day.
Artisanal mining is problematic for numerous other reasons too. The use of child labour in the industry is prominent, work hours are long especially for women, and the methods used in small-scale mining include exposure to mercury, dust, and fumes which make operations hazardous to both miners and the environment. Mining communities also tend to suffer disproportionately from communicable diseases, poor sanitation, sexual violence and food insecurity, and artisanal miners are often forced to live like nomads moving frequently to seek out new opportunities.
In conflict-free zones such as Katanga's Likasi and Lubumbashi, ASM miners sell to numerous stakeholders, including various large-scale mining (LSM) companies, the state-owned Gécamines, security forces, militia groups and local communities. In these exchanges, there is often exploitation or extortion by government officials, as well as fraud, corruption, bribery, and theft.
Despite the countless problems associated with artisanal mining, a 2007 report by the global networking and coordination facility Communities and Small Scale Mining (CASM) emphasises the importance of these practices to individuals, the mining sector and to the broader economy. "This disorganised and recalcitrant sector probably provides a vital livelihood to many thousands of people dispersed throughout the country, and collectively probably constitutes over 80% of the entire mining sector production", the report explains.
Many of the structural reasons behind the DRC's lack of development and rise of small-scale informal mining can be clearly located in the country's recent past. During Mobutu Sese Seko's three-decade rule, the country's economy sunk and infrastructure collapsed to unprecedented lows. The state-owned La Générale des Carrières et des Mines (Gécamines), which employed thousands of miners, was virtually bankrupt by the end of Mobutu's kleptocratic rule largely as a result of theft by army "guards." And, moreover, two successive wars enabled systematic exploitation of the country's resources by local militias and foreign armies.
Some recovery took place under Laurent Kabila who overthrew Mobutu in 1997, and Gécamines expanded, but it has never fully recovered. External investments began to flow in and Chinese and other foreign companies started investing in the large-scale mining sector, providing new opportunities and hope for change. A large segment of the population has, however, continued to work in the ASM sector.
Despite the fall in mineral prices, mining is still one of the few economic lifelines in the DRC and changes in governance and policymaking around mining are becoming ever more pressing. ASM activity is still unregulated due to lack of law enforcement, corruption still looms, and in almost all regions artisanal miners are regularly abused and subjected to a disproportionate informal taxes and fees. These problems could well worsen unless changes are made and put into effect immediately.


It is official. The couple who got engaged this year, are all set to tie the knot on 25th January 2015. So far, only the date has been declared. Will it be a big fat wedding like Saif and Kareena or a simple and austere wedding, with only close family, is yet to be seen. The couple wants a simple wedding, though.


text:
Two Randolph County men sentenced on federal firearm charges
Kyle Inselmann, 34, of Steeleville, has been sentenced to serve 10 years in federal prison for knowingly possessing a pair of short-barreled shotguns in furtherance of drug trafficking activities. Inselmann pleaded guilty to the charges last September.
According to documents filed in court, police executed a search warrant at Inslemann's Randolph County home in March 2019. During the search, officers discovered several guns in close proximity to 11 grams of methamphetamine.
Justin Martin, 40, of Coulterville, has been sentenced to serve five years and seven months in federal prison for knowingly possessing seven stolen firearms and using them to acquire illegal drugs.
The guns at issue were stolen from a licensed firearm distributor in Perry County in September 2018. According to court documents, Martin and others planned the theft in advance and later sold the guns to a drug dealer for seven grams of methamphetamine and $500 in cash.
For more on both of these cases, please see this week's print edition.
Baldwin still mulling over allowing marijuana


New To Heavy Hitters – Heavy Hitters Cages and Training Ltd.
Heavy Hitters Cages and Training T-Shirts are available NOW!
Each Shirt is on sale for $10/each. Come and get yours!!
Heavy Hitters is now a proud dealer of PEI Bat Company, they are a Canadian Wood Bat maker!
we will be doing an order at the end of February 2018 - so come and get your new Canadian Made WOOD BAT!!!
We are pleased to announce that we are now dealers of Lizard Skins!!! We carry a stock of bat grips and batting gloves!
Come in and get yours today!!!!!!!
Equipment and all things baseball will be available soon.


Ubisoft Forums > 1C:Maddox Games > IL2 Maddox General Discussion > Baby a wounded engine?
View Full Version : Baby a wounded engine?
I don't have a LOT of experience with this game yet and I haven't been able to tell how best to handle an engine when it starts running rough (brown smoke).
1) Run the engine at high a setting as possible so as to cover as much ground as possible before the engine quits?
2) Run the engine as gently as possible to maintain altitude so that it lasts as long as possible?
The brown smoke I assume is loss of oil? So this causes the engine to overheat?
I'm thinking that I want to run the engine fast so that I get as much distance out of it as possible before the oil runs out.
Anyone have any pointers on this or insights into how the game handles engine siezure? Does it sieze up faster if it's run harder or is the siezure just a timer thing?
If I'm at high altitude I baby the engine and aim for just enough throttle to stay off the ground 'til I'm over friendly territory. Once I'm over friendly territory I maintain enough altitude to bail out (700m +) and try for base. If the engine quits before base I'll land if there's a clear patch or bail.
At low altitude, I gun it (but less than 100%) and hope for a clear spot to land when it quits.
The best option is not to damage the engine in the first place. If you're damaging without getting shot, you're mis-handling engine management and need to run lower throttle or prop pitch settings. The commonest non-guns related causes of engines running rough are overheating or over revving. Also on the list are running supercharger 2nd/3rd stages at lower altitudes and in the German aircraft not having "WEP" engaged before increasing the throttle through 100%.
If you're suffering from an oil leak, I suggest you drop prop pitch and throttle to a minimium required to glide at high speed, usually 70% to 80%, and coast out of the combat area ASAP. Running the engine at full power would only have it seize up faster and at shorter distance than gliding it back.
Also making the mixture to high when at to higher altitude will do it. I found in the Ki84, putting the mixture to high, to high up makes the engine go bang.
Well, I was thinking that if oil is leaking out, it's better to get as much quick distance with the engine as you can while there is still any oil at all... No?
Nah mate....doesnt work that like. Gunning it only gets ya a nice POW camp wall.
when you see brown smoke coming out that usually means your fuel mixture is too rich. cut the mixture to around 80% and it will run better. you only have to do this in some A/C when you're over 5000m.
I tend to nurse the engine a bit if I can. Of course, if I'm under fire, I just hit the maximum and try and escape/run the engine into the ground.
If its anything except a German plane, reduce the RPM's through the prop pitch control, reduce the throttle, open the radiator wide, and try and keep the engine as cool as possible.
In the German planes I just reduce the throttle and open the radiator. I figure the RPM controls will take care of themselves...unless the governor is damaged at which point I run it at a minimal pitch setting.
I have noticed that in several British planes (notably the Hurricanes), that you can keep a damaged engine running by using prop pitch instead of throttle. The Hurri engine likes to over-rev when damaged in this game and prop pitch keeps it under control much better than throttle. Set the throttle for 50-60% power and listen to your engine constantly. Decrease pitch if it starts to rev too much and increase pitch if it starts to fade. I can baby a Hurri back to base 40-50 km in some cases (OK, usually not, but I have done it). Never tried it in any other planes a they don;t seem to over rev like the Hurris.
This works fine if the only damage is to the automatic propellor governor. I don't think it would get you that far with a coolant or oil loss as well.
When it starts to lose oil, you're doomed. Engine will (not ingame) overheat fast and stop. I don't know if babying the engine is modeled in game, but I wonder how much time it would give you. Generally, I run them close to overheating, and when I noice there's no power left - feather the prop. Don't know about He111, but getting home and landing on one engine isn't to hard when you're flying Bf110 or P-38.
I have tried everything (at the time on P40E) -- no good.
Is it really possible to feather the props of the 110 and 111 in this game? How? I've tried, but can't see any change what so ever..
So back to the original question - does it do any good to baby the engine in the game if you have an oil leak?
Is this modelled in the game or did Oleg just give you X amount of time for the engine to run after an oil leak? If you have X amount of time regardless of how hard you work the engine, then it makes sense to go to 110% of power and go as fast or climb as high as you can with the time you have left.
Anyone care to test this? It should be easy enough to do. Set up a mission where you take your favorite plane and get behind a bomber. Let him shoot you in the engine and start an oil leak. Baby it. Time how long the engine lasts. Repeat the scenario, but this time go to max power. Compare your times. You may want to repeat each scenario several times to get a good average.
I just vote for X ammount of time. It would be realistic.
Its far from 'easy' to test this, or someone would have done it already. The reason its not easy is the multiple types/levels of engine damage. There are most certainly times when severe engine damage occurs with a very short life remaining, and other times when damage is minimal and it will run for a very long time. It is arguable as to what types of damage are modeled, however I feel their is fuel system, oil system and coolant systems modeled, as well as prop governors and cylinder compression, with varying degrees of severity for each. Thats a lot of variables.
In short, imho, there is no way to know whats best (as it should be), beyond experience and intuition.


text:
Machiavel
Find sources: "Machiavel" – news · newspapers · books · scholar · JSTOR (March 2008) (Learn how and when to remove this template message)
This article may be written from a fan's point of view, rather than a neutral point of view. Please clean it up to conform to a higher standard of quality, and to make it neutral in tone. (January 2018) (Learn how and when to remove this template message)
www.machiavel.be
Machiavel, named after Niccolò Machiavelli, is a Belgian rock group founded in 1974 and still recording and touring today. Its first few albums are typical of the progressive rock movement while the later albums are harder.
1.1 The first years
1.2 New directions
1.3 Rebirth
2 Band members
3 Discography
The first years[edit]
Machiavel is a Belgian rock group formed in 1976 by Roland De Greef (bass) and Marc Ysaÿe (drums and vocals) then playing with a group called Moby Dick and Jack Roskam (guitar) and Albert Letecheur (keyboard). They released their first eponymous album in 1976 and gained some attention with the song "Cheerlessness" displaying Marc's high pitch voice and Albert's heady keyboard line.
Jack Roskam was soon replaced by Jean-Paul Devaux and Mario Guccio joined the group as lead vocalist and showman. This new team released Jester in 1977, a wonderful album totally in line with the so-called Eurock production of the time (Genesis, Supertramp). Jester featured attractive tunes like "Moments" with complex and ambitious creations like "Rock, Sea and Tree".
The release of Mechanical Moonbeams in 1978 confirmed the group's musical maturity and made them absolute rock-stars in their homeland. The album featured melodic songs like "Rope Dancer" or "Mary", much harder tunes such as "Beyond The Silence", complex, typical Eurock songs such as "The Fifth Season", but also some complex songs like "After The Crop", which evolves from acoustic-melodic to punk-like via Eurock and pure-rock, all in the same song. The cover was in the same line as Jester but this time in colour.
Urban Games was probably the band's most successful album. It attempted to move away from the Eurock style and proposed songs leaning on disco ("Dancing Heroes"), hard-rock ("The Dictators"), reggae ("Over the Hills"), and Eurock still ("City Flowers"). The rift between the tastes and ideas of the different members of the group was all too apparent in the group and Albert Letecheur and Jean-Paul Devaux left the group in 1979.
New directions[edit]
The well titled album New Lines, with Thierry Plas on lead guitar, was released in 1980. The single "Fly" was an immediate success but the group's next attempt with the album Break Out produced by Derek Laurence in 1981 did not meet success and the group went undercover for a number of years, if one excepts the album The Cry of Pleasure in 1987.
Rebirth[edit]
The rebirth happened in 1999 with Virtual Sun followed by Welcome to Paradise in 2003 and 2005 in 2005. Although live performances have been promoted the band fails to recapture its former glory after such a long absence. In 2010 Thierry Plas left the band because of musical differences and went in search of other projects. A new album Eleven was released featuring new guitarist Christophe Pons.
Members are active in radio and promoting and supporting new acts, especially in the Belgian market, which has become two markets due to schisms among the two major linguistic groups in Belgium, which operate separate radio and television systems.
Their lead vocalist since 1977, Mario Guccio, died at the age of 64, on 21 January 2018.[1]
Band members[edit]
Marc Ysaÿe - lead vocals, drums
Jack Roskam - guitar
Albert Letecheur - keyboards
Roland De Greef - bass
Jean-Paul Devaux - guitar
Mario Guccio - lead vocal
Thierry Plas - guitar
Paolo Regatzu - keyboards
Hervé Borbé - keyboards
Christophe Pons - guitar
Discography[edit]
Initial release are in bold. Compilations are in italic. Re-release are not highlighted.
Machiavel (EMI, 1976, LP Vinyl)
Jester (EMI, 1977, LP Vinyl)
Mechanical Moonbeams (EMI, 1978, LP Vinyl)
Urban Games (EMI, 1979, LP Vinyl)
New Lines (EMI, 1980, LP Vinyl)
Break Out (EMI, 1981, LP Vinyl)
Valentine's Day (EMI, 1982, LP Vinyl)
The Cry Of Pleasure (INDISC, 1987, LP Vinyl)
The Best of Machiavel (EMI, 1991, CD)
Machiavel (EMI, 1993, CD)
Jester (EMI, 1993, CD)
Mechanical Moonbeams (EMI, 1993, CD)
Urban Games (EMI, 1993, CD)
New Lines (EMI, 1993, CD)
20th Anniversary Machiavel - The Very Best Of (EMI, 1996, CD)
Virtual Sun (CNR Arcade, 1999, CD)
Machiavel Live (EMI, 1999, CD)
Break Out (Hans Kuster Music, 2000, CD)
Original Hits (EMI, 2000, CD)
Anthology (EMI, 2001, CD)
Welcome to Paradise (CNR Arcade, 2003, CD)
The Essential of Machiavel (2003, EMI, CD)
Mechanical Moonbeams (2005, EMI, CD)
2005 (2005, Bang, CD)
Best of Machiavel (2006, More Than Télémoustique, CD)
Acoustic (2009, Moonzoo Music, CD)
Eleven (2011, Moonzoo Music, CD)
Colours (2013, Moonzoo Music, CD)
Belgian rock
^ "Zanger van Machiavel overleden". Focus.knack.be. 21 January 2018. Retrieved 22 January 2018.
The official Machiavel site
GND: 10277193-5
MusicBrainz: 88ecfc31-b618-4704-83f3-45e16b2433ba
Retrieved from "https://en.wikipedia.org/w/index.php?title=Machiavel&oldid=876142433"
Belgian rock music groups
Belgian pop music groups
Articles needing additional references from March 2008
Articles with a promotional tone from January 2018
All articles with a promotional tone
Wikipedia articles with style issues from January 2018
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Articles with multiple maintenance issues
This page was last edited on 31 December 2018, at 12:40 (UTC).


text:
\section{Conclusion}
This paper studies the problem of state generation for multi-domain dialogues. Existing generation-based models fail to model the dialogue dependencies and ignore the slot-overlapping problem in MDST. To overcome the limitation of existing models, we present novel Parallel Interactive Networks (PIN) for more accurate and robust dialogue state generation. The design of the PIN model is inspired by the interactive nature of the dialogues and the overlapping slots in the ontology. The cross-turn dependencies and the in-turn dependencies are characterized by the Interactive Encoder. The slot-overlapping problem are solved by introducing the slot-level context. Further more, a distributed copy mechanism is introduced to perform a selective copy from either the historical system utterances or the historical user utterances. Empirical studies on two benchmark datasets demonstrate the effectiveness of the PIN model.

\section{Experiment}
\subsection{Datasets}
\noindent {\bf MultiWOZ 2.0.} The Multi-Domain Wizard-of-Oz (MultiWOZ 2.0) dataset, collected by ~\cite{Budzianowski:18}, with conversations spanning over multiple domains and topics, is used to train and evaluate the models.
There are total 7 domains with 30 $(domain, slot)$ pairs in the ontology; these $(domain, slot)$ pairs involve $4,510$ values. The dataset contains $10,419$ dialogues with total $115,434$ turns; the average turns of dialogue is $13.46$.
The training, validation and test set contain $8,420$, $1,000$ and $1,000$ dialogues respectively.
As is mentioned in ~\cite{Wu:19} that {\em hospital} and {\em police} domain have very few dialogues and only appear in the training set. We thus follow the dataset setting in ~\cite{Wu:19} that only keep five domains ({\em restaurant}, {\em hotel}, {\em attraction}, {\em taxi}, {\em train}) in the experiment.

\noindent {\bf MultiWOZ 2.1.} As is pointed out in~\cite{Eric:19}, the MultiWOZ 2.0 dataset is faulty in substantial errors in the state annotations and dialogue utterances. In order to clean the dataset, the authors of ~\cite{Eric:19} ask crowd-source workers to fix the state annotations and utterances in the original data. As a result, over 32\% of state annotations in 40\% of the dialogue turns are changed and 146 utterances are fixed. The cleaned dataset are released as the MultiWOZ 2.1 dataset. We also evaluate our models on the MultiWOZ 2.1 dataset.
\subsection{Implementation Details}
The proposed model is implemented using the Pytorch deep learning framework. All the embeddings of the words in the vocabulary are initialized by the concatenation of the pre-trained GloVe embeddings~\cite{Pennington:14} and character n-gram embeddings~\cite{Hashimoto:17}. We tune the hyper-parameters of the models by grid search on validation set. The batch size is set as 32. The dimensions of hidden states in all GRUs are set as $400$. The embedding dropout is used in the Interactive Encoder with dropout rate $0.3$. Following~\cite{Bowman:16, Wu:19}, we also adopt the word dropout in the Interactive Encoder to improve the model generalization; and the dropout rate is set as $0.3$. At training time, the Value Generator uses Teacher-forcing~\cite{Williams:89} with a probability $0.5$. The greedy search~\cite{Vinyals:15} is used in the decoding process. We use the Adam optimizer~\cite{Kingma:15} with initialized learning rate $0.001$ to optimize the model.

\subsection{Evaluation Metrics}
The standard metrics joint goal accuracy and goal accuracy are used to evaluate the performance of the multi-domain dialogue state tracking. The joint goal accuracy denotes the proportion of dialogue turns where the values of all the $(domain, slot)$ pairs are correctly predicted. While the goal accuracy is the proportion of the correctly predicted $(domain, slot, value)$ triplets in total test set.

\subsection{Baseline Models}
The recently proposed dialogue state tracking models are used for comparison.
The models dealing with dialogue state tracking through building classifiers on predefined ontology include the MDBT~\cite{Ramadan:18}, GLAD~\cite{Zhong:18}, GCE~\cite{Nouri:18}, SUMBT~\cite{Lee:19}, FJST~\cite{Eric:19} and HJST~\cite{Eric:19}. The models utilizing copy mechanism to generate the dialogue state include PtrNet~\cite{Xu:18}. The models incorporating both state classifiers and state copy system include HyST~\cite{Goel:19}, DSTreader~\cite{Gao:19}, TRADE~\cite{Wu:19} and DST-Picklist~\cite{Zhang:19}.

\subsection{Experimental Results}
\noindent {\bf Evaluation on the MultiWOZ 2.0 dataset.} The evaluation results on the MultiWOZ 2.0 dataset are shown in Table ~\ref{tab:multiWOZ2.0}. We observe from the table that most of the models building classifiers on predefined ontology and the models generating the dialogue states through copy mechanism are inferior to the models utilizing both the classifiers and the copy system. As is mentioned in ~\cite{Eric:19} that the models building upon a copy system have advantage in extracting values from the dialogue history but struggle to predict values that are not exist in the dialogue history. Thus it is reasonable that models combining copy system with state classifiers achieve better performance. Comparing with the current state-of-the-art model TRADE, PIN achieves significant 3.82\% performance gain. This fact demonstrates that the modeling of the interaction dependencies, the slot-level context and the distributed copy mechanism are beneficial for improving state generation.
\begin{table}[H]
	\centering
	\caption{Evaluation on the MultiWOZ 2.0 dataset.}
	\label{tab:multiWOZ2.0}
	\begin{tabular}{ccc}
		\toprule
		{\bf Model}&{\bf Joint Goal} (\%) & {\bf Goal} (\%) \\
		\midrule
		MDBT& 15.57 & 89.53\\
		PtrNet & 30.28 & 93.85\\
		GLAD & 35.57 & 95.44\\
		GCE & 36.27 & {\bf 98.42}\\
		HJST & 38.40 & -\\
		DSTreader & 39.41 & -\\
		FJST & 40.20 & -\\
		HyST & 42.33 & -\\
		HyST(ensemble) & 44.22 & -\\
		SUMBT & 42.40 & -\\
		DSTreader+JST & 47.33 & -\\
		TRADE & 48.62 & 96.92\\
		{\bf PIN} & {\bf 52.44} & 97.28\\
		\bottomrule
	\end{tabular}
\end{table}
\noindent {\bf Evaluation on the MultiWOZ 2.1 dataset.} The evaluation results on the MultiWOZ 2.1 dataset are shown in Table ~\ref{tab:multiWOZ2.1}. The consistent performance drop is caused by the changing of a value to a {\em dontcare} or {\em none} label as explained in ~\cite{Eric:19}.
The PIN model outperforms the previous models by a significant margin except for the DST-Picklist model, which indicates the effectiveness of the model design. Although DST-Picklist achieves the state-of-the-art, it takes a lot human efforts in dividing the slots into span-based slots or picklist-based slots.

\begin{table}[H]
  \centering
  \caption{Evaluation on the MultiWOZ 2.1 dataset.}
  \label{tab:multiWOZ2.1}
  \begin{tabular}{ccc}
    \toprule
    {\bf Model}&{\bf Joint Goal} (\%) & {\bf Goal} (\%) \\
    \midrule
    HJST & 35.55 & -\\
    DST Reader & 36.40 & -\\
    FJST & 38.00 & -\\
    HyST & 38.10 & -\\
    TRADE & 45.60 & 96.55\\
    DST-Picklist & 53.30 & -\\
    {\bf PIN} & {\bf 48.40} & {\bf 97.02}\\
  \bottomrule
\end{tabular}
\end{table}

\subsection{Evaluation on the Overlapping Slots}
In multi-domain dialogue state tracking, domains may have overlapping slots. One of the motivations for building the PIN model is to handle the slot overlapping problem with slot-level context. Thus we report the goal accuracy on each overlapping slot in Table~\ref{tab:overlap} for further analysis on PIN. Table~\ref{tab:overlap} shows that slot overlapping usually appears among similar domains, such as ({\em Restaurant}, {\em Hotel}, {\em Attraction}) and ({\em Train}, {\em Taxi}). The PIN model achieves higher goal accuracy than TRADE on all overlapping slots. This result demonstrates the effectiveness of the slot-level context on extracting distinctive features for each slot, so that the values for overlapping slots are correctly predicted.
\begin{table}[H]
	\centering
	\caption{The evaluation results for overlapping slots on the MultiWOZ 2.1 dataset. The involved domains are 1:{\em Restaurant}, 2:{\em Hotel}, 3:{\em Attraction}, 4:{\em Train}, 5:{\em Taxi}.}
	\label{tab:overlap}
	\begin{tabular}{cccc}
		\toprule
		{\bf Slot} & {\bf Domains} & {\bf TRADE} & {\bf PIN}\\
		\midrule
		area& 1,2,3 & 86.2 & {\bf 86.4} \\
		book people& 1,2,3 & 92.0 & {\bf 95.1}\\
		price range& 2,3 & 84.2 & {\bf 89.7}\\
		book day& 2,3 & 96.4 & {\bf 96.8}\\
		departure& 4,5 & 89.0 & {\bf 90.9}\\
		destination& 4,5 & 91.6 & {\bf 92.4}\\
		leave at& 4,5 & 65.1 & {\bf 66.7}\\
		arrive by& 4,5 & 82.4 & {\bf 84.7}\\
		\bottomrule
	\end{tabular}
\end{table}

\subsection{The Effectiveness of the Interactive Encoding}\label{sec:sigleDomain}
In order to study the function of the Interactive Encoder in handling in-turn and cross-turn dependencies existing in MDST, we use a subset of the testing data for error analysis. We first sample 100 dialogue turns from the test set of the MultiWOZ 2.1 dataset. Then the wrongly predicted $(domain, slot, value)$ triplets for the TRADE and PIN model are selected and each of the triplets is marked according to the dependencies (in-turn or cross-turn) it is involved in the dialogue. The statistics of these error predictions are shown in Figure ~\ref{fig:bar}. From the figure, we observe that whether in the dialogue turns involving in-turn dependencies or cross-turn dependencies, the PIN model creates much less prediction errors than the TRADE model, especially on $hotel$ domain and $restaurant$ domain. These results demonstrate the effectiveness of the Interactive Encoder in capturing the in-turn and cross-turn dependencies.
\begin{figure}[H]
	\begin{center}
		\begin{tabular}{lr}
		\hspace{-.4cm}
			\scalebox{0.45}{
				\input{tikz_bar.tex}
			}&
		\hspace{-.4cm}
			\scalebox{0.45}{
				\input{tikz_bar1.tex}
			}
		\end{tabular}
	\end{center}
	\caption{Error analysis on the dialogue turns involving with in-turn dependencies (left) and cross-turn dependencies (right). The number of error predictions for the TRADE model and PIN model on each domain are reported.}
	\label{fig:bar}
\end{figure}

\subsection{The Function of the Distributed Copy Mechanism}\label{sec:case}
Unlike the traditional copy mechanism that only copies words from one sequence, the distributed copy mechanism in the PIN model can copy words from two separated sequences considering the interactive nature of dialogues.
The example in Figure~\ref{fig:case} shows a case that the traditional copy mechanism will make a wrong prediction but the distributed copy mechanism will correctly predict. The dialogue in Figure~\ref{fig:case} is sample from the Restaurant domain in the test set. In this example, we want to predict the value of the {\em food} slot. As the wight $\alpha=0.37$, the generator has higher probability to copy a word from the dialogue history. In the total dialogue history, if we ignore the wight $\beta$, which determines whether to copy from the historical system utterance or from the historical user utterance, the generator will copy the wrong word {\em reservation} from the entire dialogue history because the word {\em reservation} has higher copy probability $0.668$ than $0.507$ of the word {\em european}. This wrong prediction will happens in the traditional copy-based model. But in PIN, the word to be copied also depends on the sequence-selection weight $\beta$. With a probability $0.967$ to copy the word from the historical user utterance, the correct value {\em european} will be copied according to Equation~\ref{eq:final}. This case demonstrates the effectiveness of the distributed copy mechanism.
\begin{figure*}[h!]
	\begin{center}
		\begin{tabular}{c}
			\input{tikz_case.tex}
		\end{tabular}
	\end{center}
	\caption{An example of dialogues and prediction of PIN. The red color represent the copy probability of the word. And the copy probability of the word {\em reservation} in system utterances is $0.668$, the copy probability of the word {\em european} in the user utterances is $0.507$.}
	\label{fig:case}
\end{figure*}

\section{Introduction}
Spoken dialogue system (SDS) is an application that can help users complete their goals efficiently.
A SDS usually has a logic engine, called dialogue manager, which involves two main sub-tasks for determining how the system will respond to the users: dialogue state tracking and dialogue policy learning. The task we discuss in this paper is dialogue state tracking, which allows the system maintaining a internal representation of the state of the dialogue as the dialogue progress~\cite{Young:10}.

Dialogue state tracking involving single domain has been extensively studied and achieved mach progress. As more challenging task, Multi-domain dialogue state tracking (MDST) has been introduced in~\cite{Ramadan:18} and attracts much attention in research community.
Instead of only predicting the $(slot, value)$ pair, in MDST, a model is expected to predict the $(domain, slot, value)$ triplets for each slot in each domain. This task is a great challenge not only because of the large ontology involving $30$ slots and exceeding $4500$ values~\cite{Wu:19}, but also the mixed-domain nature of the dialogues and some complex cases involving cross-turn inference.

\begin{figure}[h!]
	\begin{center}
		\begin{tabular}{c}
			\input{tikz_intro.tex}
		\end{tabular}
	\end{center}
	\caption{The dependencies between the system utterances and user utterances in a multi-domain dialogue. The red lines imply cross-turn dependencies and the blue lines imply in-turn dependencies.}
	\label{fig:intro}
\end{figure}

Several models have been proposed for MDST task and proven to be successful~\cite{Mrksic:15, Ramadan:18, Goel:19, Eric:19, Lee:19, Wu:19}. Among these models, TRADE~\cite{Wu:19} achieves the state-of-the-art on the MultiWOZ 2.0 dataset (one of the standard MDST datasets) by encoding the entire dialogue history using a bidirectional GRU and incorporating soft-gated copy mechanism to generate the values. Inspired by TRADE, we purpose to build a more accurate and robust state generator PIN. The motivations of proposing PIN is in two aspects.

One aspect is considering the interactive nature of the dialogues. The interaction of the user and the system is often organized by a question-answering style. It is common in dialogue state tracking that a domain or slot being specified by one of the user or system, then the value being answered by the other. For example, in the dialogue in Figure~\ref{fig:intro}, the user specifies a {\em Restaurant} domain, and the system answers a restaurant name {\em Curry Garden}.  As is shown in Figure~\ref{fig:intro}, there are two type of dependencies, in-turn dependencies and cross-turn dependencies, both contribute to discovering slot-value pairs. It is worth noting that some hard cases involving inference actually rely on cross-turn dependencies (e.g. the dependency between utterance \textbf{s2} and \textbf{u3} in Figure~\ref{fig:intro}). Thus a correctly modelling of these dependencies can improve slot-value extraction and cross-turn inference. In this work, we build an Interactive Encoder which completely accords with the dependencies expressed in Figure~\ref{fig:intro} to jointly model the in-turn dependencies and cross-turn dependencies.

The interactive nature of dialogues also implies that the value for a slot tends to be specified frequently either by a system or by a user. For example, the values for slots involving names, such as {\em Restaurant-name} and {\em Hotel-name}, are likely to be provided by the system. And the values for the slots like {\em Hotel-stay} (the days to stay) and {\em Hotel-people} (the number of people booking for) are usually provided by the user. This observation inspires our designing of the distributed copy mechanism which allows the state generator choosing to copy words from either the historical system utterances or the historical user utterances.

The other aspect is the slot overlapping problem in MDST. Unlike single-domain DST, slot overlapping is common in MDST and these overlapping slots share the similar values. For example, both {\em Restaurant} and {\em Hotel} domain have a slot {\em price range} which shares the same values. Under this condition, a generator without considering slot-specific features may mistakenly extract the value of one slot as the value of some other slot. To overcoming the slot overlapping problem, we introduce a slot-level context in the state generator.

In summary, we propose a generation-based MDST model which takes into consideration of the interactive nature of dialogues and slot overlapping problem in MDST. The contributions of this work are as follows.
\begin{itemize}
    \item We propose a interactive encoding method with two parallel hierarchical recurrent networks which can jointly model the in-turn dependencies and cross-turn dependencies.
    \item We introduce the slot-level context into the state generator to accurately generate the values for overlapping slots.
    \item We present a distributed copy mechanism to selectively copy words from either the historical system utterances or the historical user utterances.
\end{itemize}

\section{Methodology}
In this section, we introduce the proposed PIN model. The model consists of four components: Interactive Encoder, Slot-level Context, Value Generator and Slot Gate. We next describe each component in detail.

\subsection{Interactive Encoder}
Our design of the Interactive Encoder is inspired by the dependencies between the system and user utterances. Specifically, we wish to propose a novel network structure that completely represents the dependencies expressed in Figure~\ref{fig:intro}. A hierarchical recurrent networks with specific structures has been used to construct the Interactive Encoder, as shown in Figure~\ref{fig:interEncoder}.
\begin{figure}[h!]
	\begin{center}
		\begin{tabular}{c}
			\scalebox{0.5}{
				\input{tikz_enc.tex}
			}
		\end{tabular}
	\end{center}
	\vspace{-0.4cm}
	\caption{The structure of the Interactive Encoder. Due to space limitation, only two turns are shown. The red arrows emphasize modeling cross-turn dependencies and the blue arrows emphasize modeling in-turn dependencies.}
	\label{fig:interEncoder}
\end{figure}
The Interactive Encoder consists of two parallel hierarchical recurrent networks, one for historical system utterance encoding and another for historical user utterance encoding. The lower layer of the hierarchical recurrent networks allows each word to capture the cross-turn dependencies; and the higher layer of the hierarchical recurrent networks allows each word to capture the in-turn dependencies. In this way, the cross-turn dependencies and in-turn dependencies are jointly modeled.

We now present the details of the Interactive Encoder. Let $\mathbf{A}_l=\left\{ \mathbf{a}_{1}, \mathbf{a}_{2}, \cdots, \mathbf{a}_{m} \right\}$ denotes the sequence of word embeddings for the the $l^{th}$ system utterance. And $\mathbf{U}_l=\left\{ \mathbf{u}_{1}, \mathbf{u}_{2}, \cdots, \mathbf{u}_{n} \right\}$ denotes the sequence of word embeddings for the the $l^{th}$ user utterance.  Here $m$ and $n$ denotes the number of words in the $l^{th}$ system utterance and user utterance respectively.

For later use, we introduce a notation ${\rm GRE}(\mathbf{X},\mathbf{h}; \mathbf{W})$ to indicate the bi-directional GRU encoder~\cite{Chung:14} with inputs $\mathbf{X}$ (sequence of vector representations, such as word embeddings), parameters $\mathbf{W}$ and initialized hidden state $\mathbf{h}$.
The Interactive Encoder jointly models the cross-turn dependencies and in-turn dependencies through the following recurrent process.

The Interactive Encoder first let the input word embedding sequences $\mathbf{A}_l$ and $\mathbf{U}_l$ interact with the historical context, allowing the words capturing cross-turn dependencies
\begin{equation}
\begin{split}
\mathbf{G}_{l}^{a}, \mathbf{g}_{l}^{a} = {\rm GRE}(\mathbf{A}_l,\mathbf{h}_{l-1}^u; \mathbf{W}_a) \\
\mathbf{G}_{t}^{u}, \mathbf{g}_{l}^{u} = {\rm GRE}(\mathbf{U}_l,\mathbf{h}_{l-1}^a; \mathbf{W}_u)
\end{split}
\label{eq:enc1}
\end{equation}
where $\mathbf{W}_a$ and $\mathbf{W}_u$ are the parameters of the GRUs, the initialized hidden states $\mathbf{h}_{l-1}^a$ and $\mathbf{h}_{l-1}^u$ are respectively the system context vector and the user context vector generated from the last turn. $\mathbf{G}_{l}^{a}$ and $\mathbf{g}_{l}^{a}$ denote the entire sequence of output vectors and the last output vector of the GRUs, respectively.

The outputs of the lower-layer GRUs, $\mathbf{G}_{l}^{a}$ and $\mathbf{g}_{l}^{a}$, are then feed into the higher-layer GRUs to interact with the current context for capturing in-turn dependencies
\begin{equation}
	\begin{split}
		\mathbf{H}_{l}^{a
		}, \mathbf{h}_l^a = {\rm GRE}(\mathbf{G}_{l}^{a},\mathbf{g}_{l}^{u}; \mathbf{M}_a)\\
		\mathbf{H}_{l}^{u},\mathbf{h}_l^u = {\rm GRE}(\mathbf{G}_{t}^{u},\mathbf{g}_{l}^{a}; \mathbf{M}_u)
	\end{split}
	\label{eq:enc2}
\end{equation}
where $\mathbf{M}_a$ and $\mathbf{M}_u$ are the parameters of the higher-layer GRUs, and $\mathbf{h}_{l}^a$ and $\mathbf{h}_{l}^u$ are the generated system context vector and user context vector of the current turn. $\mathbf{h}_{l}^a$ and $\mathbf{h}_{l}^u$ are then feed into the lower-layer GRUs as the initialized hidden states of the next turn.

With this recurrent architecture, the Interactive Encoder captures the dependencies of the entire dialogue history by rolling from the first turn to the current turn. At the beginning of a dialogue, we simply set the initialized hidden states as zero vectors , that is $\mathbf{h}_0^a = \mathbf{h}_0^u = \mathbf{0}$.

The outputs from each turn of the dialogue are then concatenated as the system context sequence $\mathbf{H}^{a}=\left\{ \mathbf{h}_{1}^{a}, \mathbf{h}_{2}^{a}, \cdots, \mathbf{h}_{M}^{a} \right\}$ and user context sequence $\mathbf{H}^{u}=\left\{ \mathbf{h}_{1}^{u}, \mathbf{h}_{2}^{u}, \cdots, \mathbf{h}_{N}^{u} \right\}$. Here $M$ and $N$ denote the total number of words in historical system utterance and historical user utterance respectively.

\begin{figure*}[h!]
	\begin{center}
		\begin{tabular}{c}
			\scalebox{0.7}{
				\input{tikz_dec.tex}
			}
		\end{tabular}
	\end{center}
	\caption{The architecture of the Value Generator and the Slot Gate.}
	\label{fig:dec}
\end{figure*}
\subsection{Slot-level Context} \label{sec:localContext}
The purpose of applying the slot-level context here is to strengthen the context representation with slot specific features and deal with the slot overlapping problem. We simply employ the attention mechanism to construct the slot-level context. Specifically, for each $(domain, slot)$ pair, we introduce an embedding vector $\mathbf{v}_s$. The slot-level system context $\mathbf{c}_s^a$ and the slot-level user context $\mathbf{c}_s^u$ are computed by
\begin{equation}
\begin{split}
\mathbf{c}_s^a \!= \!\sum_{i=1}^{M}\mu_{i}\mathbf{h}_{i}^{a}, \quad \!\!\mu_{i}\! =\! \frac{\exp{(\mathbf{v}_s^T}\mathbf{h}_{i}^a)}{\sum_{k=1}^{M}\exp{(\mathbf{v}_s^T}\mathbf{h}_{k}^a)}\\
\mathbf{c}_s^u\! =\! \sum_{j=1}^{N}\eta_{j}\mathbf{h}_{j}^{u}, \quad \!\!\eta_{j} \!=\! \frac{\exp{(\mathbf{v}_s^T}\mathbf{h}_{j}^u)}{\sum_{l=1}^{N}\exp{(\mathbf{v}_s^T}\mathbf{h}_{l}^u)}\\
\end{split}
\label{eq:context}
\end{equation}
The slot-level context of the entire dialogue history is then simply the summation of the slot-level system context and the slot-level user context
\begin{equation}
\begin{split}
\mathbf{c}_s = \mathbf{c}_s^a + \mathbf{c}_s^u
\end{split}
\label{eq:contextall}
\end{equation}
The slot-level context is then feed into the Value Generator as the initialized hidden state for the decoder GRU.

\subsection{Value Generator}
The Value Generator takes the slot-level context as input and uses a GRU decoder to generate the value sequence for each $(domain, slot)$ pair. Different from the copy mechanism applied in TRADE~\cite{Wu:19} that copying words from the entire dialogue history, in this paper, we propose a distributed copy mechanism that allows the state generator copying words from different sequences. The architecture of the Value Generator is shown in Figure~\ref*{fig:dec}. we now describe it in detail.

We use the abbreviation {\rm GRD} to denote the GRU decoder. At the $t^{th}$ decoding step, the hidden state of the GRU decoder for each $(domain, slot)$ pair $s$ is
\begin{equation}
\begin{split}
    \mathbf{o}_s^t = {\rm GRD}(\mathbf{x}_s^{t}, \mathbf{o}_s^{t-1}, \mathbf{W}_{d})
\end{split}
\label{eq:dec}
\end{equation}
where $\mathbf{x}_{s}^{t}$ is the input at the $t^{th}$ step, $\mathbf{o}^{t}_{s}$ is the hidden state at the $t^{th}$ step and $\mathbf{W}_{d}$ is the parameters of the GRU decoder. The hidden state of GRD for each slot is initialized with corresponding slot-level context $\mathbf{c}_{s}$. The first input $\mathbf{x}_{s}^{0}$ is set as the summation of corresponding domain embedding and slot embedding.

We then introduce three distributions on the vocabulary: $P_{s,t}^{v}$, $P_{s,t}^{a}$ and $P_{s,t}^{u}$, for applying distributed copy mechanism. The three distributions represent the
probabilities of generating a word from the vocabulary, copying a word from the historical system utterances and copying a word from the historical user utterances, respectively.
Let $\mathbf{e}_{i}$ be the embedding of the $i^{th}$ word in the vocabulary and $|V|$ be the vocabulary size. We use $P_{s,t}[i]$ to denote the $i^{th}$ element in $P_{s,t}$. Then the three distributions are computed by
\begin{equation}
\begin{split}
&P_{s,t}^{v}[i] = \frac{\exp{(\mathbf{e}_{i}^{T}\mathbf{o}_s^t)}}{\sum_{j=1}^{|V|}\exp{(\mathbf{e}_{j}^{T}\mathbf{o}_s^t)}}\\
&P_{s,t}^{a}[i] = \sum_{f(k)=i} \frac{\exp{((\mathbf{h}_{k}^{a})^{T}\mathbf{o}_s^t)}}{\sum_{j=1}^{M}\exp{((\mathbf{h}_{j}^{a})^{T}\mathbf{o}_s^t)}}\\
&P_{s,t}^{u}[i] = \sum_{f(k)=i} \frac{\exp{((\mathbf{h}_{k}^{u})^{T}\mathbf{o}_s^t)}}{\sum_{j=1}^{N}\exp{((\mathbf{h}_{j}^{u})^{T}\mathbf{o}_s^t)}}
\end{split}
\label{eq:gen}
\end{equation}
where the function $f$ is used for mapping a distribution on the dialogue-history to corresponding distribution on the vocabulary.

The three distributions, $P_{s,t}^{v}$, $P_{s,t}^{a}$ and $P_{s,t}^{u}$ are then combined by learnable weights. We define $\alpha_{s,t}$ as the weight of generating from the vocabulary and $\beta_{s,t
}$ as the weight of choosing to copy a word from the system utterances. For calculating the weights $\alpha_{s,t}$ and $\beta_{s,t}$, we first generate new feature vectors
\begin{equation}
\begin{split}
\mathbf{h}_{s,t}^{a}\!=\!\sum_{i=1}^{M}\!q_{s,t}^{a}[i]\cdot \mathbf{h}_{i}^{a},\quad\mathbf{h}_{s,t}^{u}\!=\!\sum_{j=1}^{N}\!q_{s,t}^{u}[j]\cdot \mathbf{h}_{j}^{u}
\end{split}
\label{eq:h_feature}
\end{equation}
The weight $\alpha_{s,t}$ and $\beta_{s,t}$ are then computed by
\begin{equation}
\begin{split}
    &\alpha_{s,t} = \sigma(\mathbf{W}_{v}^{T}\cdot [\mathbf{x}_s^t, \mathbf{o}_s^t, \mathbf{h}_{s,t}^{a}, \mathbf{h}_{s,t}^{u}])\\
	&\rho_{s,t}^{a} = \mathbf{W}_{c}^{T}\cdot [\mathbf{x}_s^t, \mathbf{o}_s^t, \mathbf{h}_{s,t}^{a}]\\
	&\rho_{s,t}^{u} = \mathbf{W}_{c}^{T}\cdot [\mathbf{x}_s^t, \mathbf{o}_s^t, \mathbf{h}_{s,t}^{u}]\\
	&\beta_{s,t} = \frac{\exp{(\rho_{s,t}^{a})}}{\exp{(\rho_{s,t}^{a})}+\exp{(\rho_{s,t}^{u})}}
\end{split}
\label{eq:vocweight}
\end{equation}
where $\mathbf{W}_{v}$ and $\mathbf{W}_{c}$ are the parameters of the linear functions, and $\sigma$ denotes the logistic function.

The final distribution $P_{s,t}$ is then calculated as the weighted sum of distributions $P_{s,t}^{v}$, $P_{s,t}^{a}$ and $P_{s,t}^{u}$ as follows
\begin{equation}
\begin{split}
    P_{\!\!s,t} \!=\! \alpha_{s,t}P_{\!\!s,t}^{v} \!+\! (1\!-\!\alpha_{s,t})(\beta_{s,t}P_{\!\!s,t}^{a} \!+\! (1\!-\!\beta_{s,t})P_{\!\!s,t}^{u})
\end{split}
\label{eq:final}
\end{equation}

The $t^{th}$ word of the value for $(domain,slot)$ pair $s$ is then generated from distribution $P_{s,t}$. The embedding of the generated word is then used as the next input of the GRU decoder. This generation procedure allows the state generator generating words from the vocabulary or copying words from either the historical system utterances or the historical user utterances.

\subsection{Slot Gate}
Following TRADE~\cite{Wu:19}, we introduce the slot gate to predict the special values {\em none} (the value of the slot is not expressed yet) and {\em dontcare} (the user does not care about the slot) for each $(domain, slot)$ pair. Specifically, the slot gate is a three-class classifier, which aims to identify whether the value {\em none}, {\em dontcare} or other value is expressed from the context through a softmax classifier
\begin{equation}
\begin{split}
P_s^c = \softmax(\mathbf{W}_s^T \cdot [\mathbf{h}_{s,1}^{a}, \mathbf{h}_{s,1}^{u}])
\end{split}
\label{eq:psc}
\end{equation}
where $\mathbf{W}_s$ is the parameter of the softmax classifier. For a $(domain, slot)$ pair, if the output of the slot gate is {\em none} or {\em dontcare}, the generated word sequence from the state generator will be ignored and the corresponding predicted result of the slot gate will be chosen as the value. Otherwise, the generated word sequence from the state generator will be the predicted value for the $(domain, slot)$ pair.

\subsection{Loss Function and Optimization}
The cross-entropy loss is built for optimizing both the Value Generator and the Slot Gate, simultaneously. Let $\mathcal{S}$ be the total set of $(doamin, slot)$ pairs, and $T_{s}$ be the number of words in the value for slot $s\in \mathcal{S}$. We define $\mathbf{y}_{s}^{c}$ as the ground-truth one-hot label vector of the slot gate and $\mathbf{y}_{s,t}^{v}$ as the one-hot representation of the $t^{th}$ word in the value of $s$. The loss function is then defined as
\begin{equation}
\begin{split}
    \mathcal{L} =& \sum_{s \in \mathcal{S}}\sum_{i=1}^{3}-\mathbf{y}_{s}^{c}[i]\cdot \log P_{s}^{c}[i] \\
    +& \sum_{s \in \mathcal{S}} \sum_{t=1}^{T_s} \sum_{j=1}^{|V|} -\mathbf{y}_{s,t}^{v}[j]\cdot \log P_{s,t}[j]
\end{split}
\label{eq:loss}
\end{equation}
The loss function can be optimized by stochastic gradient descent(SGD) method.
\section{Problem Statement}
In multi-domain dialogue state tracking, the {\em state} is usually expressed as a set of $(domain, slot, value)$ triplets. The {\em domain} refers to the topics of the dialogue, such as the {\em Restaurant} domain, which indicates that the dialogue involves restaurant booking. The {\em slot} is an aspect of the user's goals, such as {\em food}, {\em area} and {\em pricerange} in the restaurant-booking dialogues. And the {\em value} is the user's specific interests, such as {\em chinese} value for {\em food} slot that indicates the user is interested in the Chinese food. The dialogue state is maintained so as to track the progress of the dialogue. At each turn, the system generates a system utterance in natural language, and the user responds to the system with some sentences, referred to as user utterance. The objective of multi-domain dialogue state tracking is to predict the value of each $(domain, slot)$ pair at each turn given the historical system utterances and user utterances. In this paper, the multi-domain dialogue state tracking is treated as a sequence generation task, where each word of a value is generated from a state generator.

\section{Related Works}
The dialogue state tracking (DST) problem has attracted the research community for years. The traditional single domain dialogue state tracking that focus on predicting dialogue states on specific domain has been studied intensively and achieve remarkable success
~\cite{Thomson:10,Wang:13,Lee:16,Liu:17a,Jang:16,Shi:16,Vodol:17,Yu:15,Henderson:14,Zilka:15,Mrksic:17,Xu:18,Zhong:18,Ren:18}. Some of these models solve DST problem by incorporating a natural language understanding (NLU) module~\cite{Thomson:10,Wang:13} or jointly modeling NLU and DST~\cite{Henderson:14,Zilka:15}. These models rely on hand-crafted features or delexicalisation features, which make them difficult
to scale to realistic applications. The representation learning approach has then been employed in NBT~\cite{Mrksic:17}. Following representation learning approach, the GLAD~\cite{Zhong:18} utilizes the recurrent neural networks (RNN) and the self-attention to handle rare-slot-value problem. And the StateNet~\cite{Ren:18} model propose a multi-scale receptors to extract semantic features and use LSTM for state tracking. The generation-based approach for dialogue state tracking is first adopted in PtrNet ~\cite{Xu:18} for handling the unknown-value problem. It utilize the Pointer networks~\cite{Vinyals:15} to predict the indexes of the value sequence.

For multi-domain state tracking, the first work that involves state tracking in mixed domains is ~\cite{Mrksic:15}. This work proposes a pre-training procedure to improve the performance on a new domain. The work of ~\cite{Rastogi:17} uses bi-directional GRU to extract features and predict the value by a candidate scoring model. The MDBT~\cite{Ramadan:18} model applies multiple bi-directional-LSTM to jointly track the domain and states. It adopts semantic similarity between the ontology and utterances and allows parameter sharing across domains. The HyST~\cite{Goel:19} model combines a classification-based system and a n-gram copy-based system to deal with multi-domain dialogue state tracking problem. The FJST and HJST model presented in~\cite{Eric:19} employ flatten structured LSTM and hierarchical structured LSTM to encoding the dialogue history respectively. The TRADE model~\cite{Wu:19} combines the soft-copy mechanism to generate states and a slot gate to classify special values for each slot.

redpajama_set_name: RedPajamaArXiv