Q: What did C think about setting up the recording equipment?
Text: Grad E: I guess .
Grad A: OK , we 're on . So just make sure that th your wireless mike is on , if you 're wearing a wireless .
Grad E: Check one . Check one .
Grad A: And you should be able to see which one {disfmarker} which one you 're on by , uh , watching the little bars change .
Grad B: So , which is my bar ? Mah ! Number one .
Grad A: Yep .
Grad E: Sibilance . Sibilance .
Grad A: So , actually , if you guys wanna go ahead and read digits now , as long as you 've signed the consent form , that 's alright .
Grad E: Are we supposed to read digits at the same time ?
Grad A: No . No .
Grad E: Oh , OK .
Grad A: Each individually . We 're talking about doing all at the same time but I think cognitively that would be really difficult . {vocalsound} To try to read them while everyone else is .
Grad E: Everyone would need extreme focus .
Grad A: So , when you 're reading the digit strings , the first thing to do is just say which transcript you 're on .
Professor C: Other way . We m We may wind up with ver We {disfmarker} we may need versions of all this garbage .
Grad B: For our stuff . Yeah .
Professor C: Yeah .
Grad A: Um . So the first thing you 'd wanna do is just say which transcript you 're on .
Professor C: Yeah .
Grad A: So . You can see the transcript ? There 's two large number strings on the digits ? So you would just read that one . And then you read each line with a small pause between the lines . And the pause is just so the person transcribing it can tell where one line ends and the other begins . And I 'll give {disfmarker} I 'll read the digit strings first , so can see how that goes . Um . Again , I 'm not sure how much I should talk about {pause} stuff before everyone 's here .
Professor C: Mmm . Well , we have one more coming .
Grad A: OK . Well , why don't I go ahead and read digit strings and then we can go on from there .
Professor C: OK . Well , we can start doing it .
Grad A: Thanks . So , uh , just also a note on wearing the microphones . All of you look like you 're doing it reasonably correctly , but you want it about two thumb widths away from your mouth , and then , at the corner . And that 's so that you minimize breath sounds , so that when you 're breathing , you don't breathe into the mike . Um . Yeah , that 's good . And uh {disfmarker} So , everyone needs to fill out , only once , the speaker form and the consent form . And the short form {disfmarker} I mean , you should read the consent form , but uh , the thing to notice is that we will give you an opportunity to edit a all the transcripts . So , if you say things and you don't want them to be released to the general public , which , these will be available at some point to anyone who wants them , uh , you 'll be given an opportunity by email , uh , to bleep out any portions you don't like . Um . On the speaker form just fill out as much of the information as you can . If you 're not exactly sure about the region , we 're not exactly sure either . So , don't worry too much about it . The {disfmarker} It 's just self rating . Um . And I think that 's about it . I mean , should I {disfmarker} Do you want me to talk at all about why we 're doing this and what this project is ?
Professor C: Um , yeah .
Grad A: or {disfmarker} ?
Professor C: No . There was {disfmarker} there was {disfmarker} Let 's see . Oh {disfmarker}
Grad E: Does Nancy know that we 're meeting in here ?
Grad B: I sent an email .
Professor C: She got an emai she was notified .
Grad E: Oh yeah , she got an e Yeah , yeah .
Professor C: Whether she knows {vocalsound} is another question . Um . So are the people going to be identified by name ?
Grad A: Well , what we 're gonna {disfmarker} we 'll anonymize it in the transcript . Um , but not in the audio .
Professor C: Right .
Grad A: So the
Professor C: OK . So , then in terms of people worrying about , uh , excising things from the transcript , it 's unlikely . Since it {disfmarker} it does isn't attributed . Oh , I see , but the a but the {disfmarker} but the {disfmarker}
Grad A: Right , so if I said , " Oh , hi Jerry , how are you ? " , we 're not gonna go through and cancel out the " Jerry "s .
Professor C: Yeah . Sure .
Grad A: Um , so we will go through and , in the speaker ID tags there 'll be , you know , M - one O seven , M - one O eight .
Professor C: Right .
Grad A: Um , but uh ,
Professor C: Right .
Grad A: um , it w uh , I don't know a good way of doing it on the audio , and still have people who are doing discourse research be able to use the data .
Professor C: OK . Mm - hmm . No , I {disfmarker} I wasn't complaining ,
Grad A: Yep .
Professor C: I just wanted to understand .
Grad A: Right .
Professor C: OK .
Grad B: Well , we can make up aliases for each of us .
Grad A: Yeah , I mean , whatever you wanna do is fine ,
Professor C: Right .
Grad F: OK .
Grad A: but we find that {disfmarker} We want the meeting to be as natural as possible . So , we 're trying to do real meetings .
Professor C: OK .
Grad A: And so we don't wanna have to do aliases
Professor C: Right .
Grad A: and we don't want people to be editing what they say .
Grad B: Right .
Grad A: So I think that it 's better just as a pro post - process to edit out every time you bash Microsoft .
Professor C: Right .
Grad B: Mm - hmm .
Grad A: You know ?
Professor C: Right . Um , OK . So why don't you tell us briefly
Grad A: OK . So th
Professor C: your {disfmarker} give {disfmarker} give your e normal schpiel .
Grad A: Um . So this is {disfmarker} The project is called Meeting Recorder and there are lots of different aspects of the project . Um . So my particular interest is in the PDA of the future . This is a mock - up of one . Yes , we do believe the PDA of the future will be made of wood . Um . {comment} The idea is that you 'd be able to put a PDA at the table at an impromptu meeting , and record it , and then be able to do querying and retrieval later on , on the meeting . So that 's my particular interest , is a portable device to do m uh , information retrieval on meetings . Other people are interested in other aspects of meetings . Um . So the first step on that , in any of these , is to collect some data . And so what we wanted is a room that 's instrumented with both the table top microphones , and these are very high quality pressure zone mikes , as well as the close talking mikes . What the close talk ng talking mikes gives us is some ground truth , gives us , um , high quality audio , um , especially for people who aren't interested in the acoustic parts of this corpus . So , for people who are more interested in language , we didn't want to penalize them by having only the far field mikes available . And then also , um , it 's a very , very hard task in terms of speech recognition . Um . And so , uh , on the far field mikes we can expect very low recognition results . So we wanted the near field mikes to at least isolate the difference between the two . So that 's why we 're recording in parallel with the close talking and the far field at the same time . And then , all these channels are recorded simultaneously and framed synchronously so that you can also do things like , um , beam - forming on all the microphones and do research like that . Our intention is to release this data to the public , um , probably through f through a body like the LDC . And , uh , just make it as a generally available corpus . Um . {vocalsound} There 's other work going on in meeting recording . So , we 're {disfmarker} we 're working with SRI , with UW , Um . NIST has started an effort which will include video . We 're not including video , obviously . And uh {disfmarker} and then also , um , a small amount of assistance from IBM . Is also involved . Um . Oh , and the digit strings , this is just a more constrained task . Um . So because the general environment is so challenging , we decided to {disfmarker} to do at least one set of digit strings to give ourselves something easier . And it 's exactly the same digit strings as in TI - digits , which is a common connected digits corpus . So we 'll have some , um , comparison to be able to be made .
Professor C: OK .
Grad A: Anything else ?
Professor C: No .
Grad A: OK , so when the l last person comes in , just have them wear a wireless . It should be on already . Um . Either one of those . And uh , read the digit strings and {disfmarker} and fill out the forms . So , the most important form is the consent form , so just be s be sure everyone signs that , if they consent .
Grad B: I 'm sure it 's pretty usual for meetings that people come late ,
Grad A: Yeah .
Grad B: so you will have to leave what you set .
Grad A: Right . And uh , just give me a call , which , my number 's up there when your meeting is over .
Professor C: Yep .
Grad A: And {disfmarker} I 'm going to leave the mike here but it 's n {nonvocalsound} Uh , but I 'm not gonna be on so don't have them use this one . It 'll just be sitting here .
Grad B: Input ? Yeah . There we go .
Professor C: By the way , Adam , we will be using the , uh , screen as well .
Grad B: Yep .
Professor C: So , you know . Wow ! Organization . So you guys who got email about this {pause} oh f uh , Friday or something about what we 're up to .
Grad E: No .
Grad F: No .
Grad B: I got it .
Grad E: What was the nature of the email ?
Professor C: Oh , this was about {pause} um , inferring intentions from features in context , and the words , like " s go to see " , or " visit " , or some
Grad B: Wel - we I {disfmarker} uh {disfmarker} I {disfmarker} I {disfmarker}
Professor C: You didn't get it ?
Grad E: I don't think I did .
Professor C: I guess these g have got better filters . Cuz I sent it to everybody . You just blew it off .
Grad E: Ah .
Professor C: OK .
Grad B: It 's really simple though . So this is the idea . Um . We could pursue , um , if we thought it 's {disfmarker} it 's worth it but , uh , I think we {disfmarker} we will agree on that , um , to come up with a {disfmarker} with a sort of very , very first crude prototype , and do some implementation work , and do some {disfmarker} some research , and some modeling . So the idea is if you want to go somewhere , um , and focus on that object down {disfmarker} Oh , I can actually walk with this . This is nice . down here . That 's the Powder - Tower . Now , um , {vocalsound} we found in our , uh , data and from experiments , that there 's three things you can do . Um , you can walk this way , and come really , really close to it . And touch it . But you cannot enter or do anything else . Unless you 're interested in rock climbing , it won't do you no good standing there . It 's just a dark alley . But you can touch it . If you want to actually go up or into the tower , you have to go this way , and then through some buildings and up some stairs and so forth . If you actually want to see the tower , and that 's what actually most people want to do , is just have a good look of it , take a picture for the family , {comment} you have to go this way , and go up here . And there you have a vre really view {disfmarker} It exploded , the {disfmarker} during the Thirty years - war . Really uh , interesting sight . And um , these uh {disfmarker} these lines are , um , paths ,
Grad E: Mmm .
Grad B: or so That 's ab er , i the street network of our geographic information system . And you can tell that we deliberately cut out this part . Because otherwise we couldn't get our GIS system to take {disfmarker} to lead people this way . It would always use the closest point to the object , and then the tourists would be faced , you know , in front of a wall , but it would do them absolutely no good . So , {vocalsound} what we found interesting is , first of all , intentions differ . Maybe you want to enter a building . Maybe you want to see it , take a picture of it . Or maybe you actually want to come as close as possible to the building . For whatever reason that may be .
Grad E: What 's it {disfmarker} what 's it made out of ?
Grad B: Um , r red limestone .
Grad E: So maybe you would wanna touch it .
Grad B: Yeah , maybe you would want to touch it . Um . Okay , I {disfmarker} This , um {disfmarker} These intentions , we {disfmarker} w w we could , if we want to , call it the {disfmarker} the Vista mode , where we just want to {disfmarker} eh {disfmarker} s get the overview or look at it , the Enter mode , and the , well , Tango mode . I always come up with {disfmarker} with silly names . So this " Tango " means , literally translated , " to touch " . So {disfmarker} But sometimes the {disfmarker} the Tango mode is really relevant in the {disfmarker} in the sense that , um , if you want to , uh {disfmarker} If you don't have the intention of entering your building , but you know that something is really close to it , and you just want to approach it , or get to that building . Consider , for example , the Post Office in Chicago , a building so large that it has its own zip code . So the entrance could be miles away from the closest point . So sometimes it m m m makes sense maybe to d to distinguish there . So , um , I 've looked , uh , through twenty some {disfmarker} Uh , I didn't look through all the data . um , and there {disfmarker} there 's uh , a lot more different ways in people {disfmarker} uh , the ways people phrase how to g get {disfmarker} if they want to get to a certain place . And sometimes here it 's b it 's a little bit more obvious {disfmarker} Um . {vocalsound} Maybe I should go back a couple of steps and go through the {disfmarker}
Professor C: No , OK come in , sit down . If you grab yourself a microphone .
Grad B: You need to sign some stuff and read some digits .
Professor C: Well , you can sign afterwards .
Grad B: O or later .
Grad E: You have to al also have to read some digits .
Professor C: Afterwards .
Grad D: OK . {comment} OK . Afterwards is fine .
Grad B: They are uncomfortable . Mm - hmm .
Grad D: Really small ? OK . I see . OK .
Grad B: Yep .
Grad D: Thank you .
Grad B: OK , but that was our idea .
Professor C: And it {disfmarker} it {disfmarker} it {disfmarker} it it also has to be switched on , Nance .
Grad B: Is {disfmarker} I {disfmarker} I think {disfmarker}
Grad E: No , that one 's already on , I thought he said .
Professor C: It 's on ? OK , good .
Grad D: OK . It 's on .
Grad E: Yeah .
Grad B: OK . That was the idea . Um , people , when they w when they want to go to a building , sometimes they just want to look at it . Sometimes they want to enter it . And sometimes they want to get really close to it . That 's something we found . It 's just a truism . And the places where you will lead them for these intentions are sometimes ex in incredibly different . I {disfmarker} I gave an example where the point where you end up if you want to look at it is completely different from where {disfmarker} if you want to enter it . So , this is sort of how people may , uh {disfmarker} may phrase those requests to a {disfmarker} a {disfmarker} a mock - up system at least that 's the way they did it . And we get tons of {disfmarker} of these " how do I get to " , " I want to go to " , but also , " give me directions to " , and " I would like to see " . And um , what we can sort of do , if we look closer a closer at the {disfmarker} the data {disfmarker} That was the wrong one . um , we can look at some factors that may make a difference . First of all , very important , and um , that {disfmarker} I 've completely forgot that when we talked . This is of course a crucial factor , " what type of object is it ? " So , some buildings you just don't want to take pictures of . Or very rarely . But you usually want to enter them . Some objects are more picturesque , and you {disfmarker} more f more highly photographed . Then of course the {disfmarker} the actual phrases may give us some idea of what the person wants . Um . Sometimes I found in the {disfmarker} Uh , looking at the data , in a superficial way , I found some s sort of modifiers that {disfmarker} that m may also give us a hint , um , " I 'm trying to get to " Nuh ? " I need to get to " . Sort of hints to the fact that you 're not really sightseeing and {disfmarker} and just f there for pleasure and so forth and so on . And this leads us straight to the context which also should be considered . That whatever it is you 're doing at the moment may also inter influence the interpretation of {disfmarker} of a phrase . So , this is , uh , really uh , uh , uh {disfmarker} My suggestion is really simple . We start with , um {disfmarker} Now , Let me , uh , say one more thing . What we do know , is that the parser we use in the SmartKom system will never differentiate between any of these . So , basically all of these things will result in the same XML M - three - L structure . Sort of action " go " , and then an object .
Grad D: Mm - hmm .
Grad B: Yeah ? and a source . So it 's {disfmarker} it 's {disfmarker} it 's way too crude to d capture those differences in intentions . So , I thought , " Mmm ! Maybe for a deep understanding task , that 's a nice sort of playground or first little thing . " Where we can start it and n sort of look {disfmarker} " OK , we need , we gonna get those M - three - L structures . The crude , undifferentiated parse . Interpreted input . We may need additional part of speech , or maybe just some information on the verb , and modifiers , auxiliaries . We 'll see . And I will try to {disfmarker} to sort of come up with a list of factors that we need to get out of there , and maybe we want to get a g switch for the context . So this is not something which we can actually monitor , {vocalsound} now , but just is something we can set . And then you can all imagine sort of a {disfmarker} a constrained satisfaction program , depending on {disfmarker} on what , um , comes out . We want to have an {disfmarker} a structure resulting if we feed it through a belief - net or {disfmarker} or something along those lines . We 'd get an inferred intention , we {disfmarker} we produce a structure that differentiates between the Vista , the Enter , and the , um , Tango mode . Which I think we maybe want to ignore . But . That 's my idea . It 's up for discussion . We can change all of it , any bit of it . Throw it all away .
Grad F: Now @ @ this email that you sent , actually .
Professor C: What ?
Grad F: Now I remember the email .
Professor C: OK .
Grad E: Huh . Still , I have no recollection whatsoever of the email . I 'll have to go back and check .
Professor C: Not important . So , what is important is that we understand what the proposed task is . And , the {disfmarker} the i uh , Robert and I talked about this some on Friday . And we think it 's well - formed . So we think it 's a well - formed , uh , starter task for this , uh , deeper understanding in the tourist domain .
Grad F: So , where exactly is the , uh , deeper understanding being done ? Like I mean , s is it before the Bayes - net ? Is it , uh {disfmarker}
Professor C: Well , it 's the {disfmarker} it 's {disfmarker} it 's always all of it . So , in general it 's always going to be , the answer is , everywhere . Uh , so the notion is that , uh , this isn't real deep . But it 's deep enough that you can distinguish between these th three quite different kinds of , uh , going to see some tourist thing . And , so that 's {disfmarker} that 's the quote " deep " that we 're trying to get at . And , Robert 's point is that the current front - end doesn't give you any way to {disfmarker} Not only doesn't it do it , but it also doesn't give you enough information to do it . It isn't like , if you just took what the front - end gives you , and used some clever inference algorithm on it , you would be able to figure out which of these is going on . So , uh , and this is {disfmarker} Bu - I in general it 's gonna be true of any kind of deep understanding , there 's gonna be contextual things , there 're gonna be linguistic things , there 're gonna be discourse things , and they gotta be combined . And , my idea on how to combine them is with a belief - net , although it may turn out that t some totally different thing is gonna work better . Um , the idea would be that {vocalsound} you , uh , take your {disfmarker} You 're editing your slide ?
Grad B: Yeah . As i a sort of , as I get ideas , uh w uh .
Professor C: Oh .
Grad B: So , discourse {disfmarker} I {disfmarker} I {disfmarker} I thought about that . Of course that needs to sort of go in there .
Professor C: Oh . I 'm sorry . OK . So . This is minutes {disfmarker} taking minutes as we go , in his {disfmarker} in his own way .
Grad B: Yep .
Professor C: Um , but the p the {disfmarker} Anyway . So the thing is , {vocalsound} i uh , d naively speaking , you 've {disfmarker} you 've got a {disfmarker} for this little task , a belief - net , which is going to have as output , the conditional pr probability of one of three things , that the person wants to {disfmarker} uh , to View it , to Enter it , or to Tango with it . Um . So that {disfmarker} the {disfmarker} the output of the belief - net is pretty well formed . And , then the inputs are going to be these kinds of things . And , then the question is {disfmarker} there are two questions {disfmarker} is , uh , one , where do you get this i {comment} information from , and two , what 's the structure of the belief - net ? So what are the conditional probabilities of this , that , and the other , given these things ? And you probably need intermediate nodes . I {disfmarker} we don't know what they are yet . So it may well be that , uh , for example , that , uh , knowing whether {disfmarker} Oh , another thing you want is some information abou I think , about the time of day . Now , they may wanna call that part of context .
Grad B: Mm - hmm .
Professor C: But the time of day matters a lot . And , if things are obviously closed , then , you {disfmarker}
Grad B: People won't want to enter it .
Professor C: Pe - people don't wanna enter them . And , if it 's not obvious , you may want to actually uh , point out to people that it 's closed {disfmarker} you know , what they 're g going to is closed and they don't have the option of entering it .
Grad B: s b
Professor C: So another thing that can come up , and will come up as soon as you get serious about this is , that another option of course is to have a {disfmarker} more of a dialogue . So if someone says something you could ask them .
Grad E: Yeah .
Professor C: OK . And {disfmarker} Now , one thing you could do is always ask them , but that 's boring . And it also w it also be a pain for the person using it . So one thing you could do is build a little system that , said , " whenever you got a question like that I 've got one of three answers . Ask them which one you want . " OK . But that 's , um , not what we 're gonna do .
Grad B: But maybe that 's a false state of the system , that it 's too close to call .
Professor C: Oh yeah . You want the {disfmarker} you want the ability to a You want the ability to ask , but what you don't wanna do is onl build a system that always asks every time , and i That 's not getting at the scientific problem ,
Grad B: Mm - hmm .
Professor C: and it 's {disfmarker} In general you 're {disfmarker} you know , it 's gonna be much more complex than that . a This is purposely a really simple case .
Grad B: Yeah .
Professor C: So , uh {disfmarker} Yeah .
Grad B: I have one more point to {disfmarker} to Bhaskara 's question . Um , I think also the {disfmarker} the {disfmarker} the deep understanding part of it is {disfmarker} is going to be in there to the extent that we um , want it in terms of our modeling . We can start , you know , basic from human beings , model that , its motions , going , walking , seeing , we can mem model all of that and then compose whatever inferences o we make out of these really conceptual primitives . That will be extremely deep in the {disfmarker} in {disfmarker} in {disfmarker} in my understanding .
Professor C: Yeah . S so {disfmarker} so the way that might come up , if you wanna {disfmarker} Suppose you wanted to do that , you might say , " Um , as an intermediate step in your belief - net , is there a Source - Path - Goal schema involved ? " OK ? And if so , uh , is there a focus on the goal ? Or is there a focus on the path ? or something . And that could be , uh , one of the conditiona you know , th the {disfmarker} In some piece of the belief - net , that could be the {disfmarker} the appropriate thing to enter .
Grad F: So , where would we extract that information from ? From the M - three - L ?
Professor C: No . No . See , the M - three - L is not gonna give th What he was saying is , the M - three - L does not have any of that . All it has is some really crude stuff saying , " A person wants to go to a place . "
Grad F: Right .
Grad E: The M - three - L is the old SmartKom output ?
Professor C: Right . M - three well , M - three - L itself refers to Multimedia Mark - up Language .
Grad E: OK . It 's just a language . Right , yeah .
Professor C: So we have th w we we we have to have a better w way of referring to {disfmarker}
Grad B: The parser output ?
Professor C: Mm - hmm .
Grad B: " Analyzed speech " I think it 's what they call it ,
Professor C: Yeah . The {disfmarker} Well , OK .
Grad B: really , oder {disfmarker}
Professor C: Yeah .
Grad B: o th No , actually , intention lattices is what we 're gonna get .
Professor C: Is - i but they c they call it intention lattice , but tha
Grad B: In - in a intention lattice k Hypothesis .
Professor C: Anyway .
Grad B: They call it intention hypotheses .
Professor C: Right . So , th they 're gonna give us some cr uh {disfmarker} or {disfmarker} We can assume that y you get this crude information . About intention , and that 's all they 're going to provide . And they don't give you the kind of object , they don't give you any discourse history , if you want to keep that you have to keep it somewhere else .
Grad B: Well , they keep it . We have to request it .
Professor C: Right .
Grad B: Nuh ? But it 's not in there .
Professor C: Well , they {disfmarker} they kee they keep it by their lights .
Grad B: Hmm .
Professor C: It may {disfmarker} it may or may not be what {disfmarker} what we want .
Grad B: Yeah , or i
Professor C: Yeah .
Grad E: So , if someone says , " I wanna touch the side of the Powder - Tower " , that would {disfmarker} basically , we need to pop up Tango mode and the {disfmarker} and the directions ?
Professor C: If i if {disfmarker} Yeah , if it got as simple as that , yeah .
Grad E: Yeah .
Professor C: But it wouldn't .
Grad E: OK . But that doesn't necessarily {disfmarker} But we 'd have to infer a Source - Path - Goal to some degree for touching the side , right ?
Grad B: Well {disfmarker} Uh , th the there is a p a point there if I understand you . Correct ? Um , because um , sometimes people just say things {disfmarker} This you find very often . " Where is the city hall ? " And this do they don't wanna sh see it on a map , or they don't wanna know it 's five hundred yards away from you , or that it 's to the {disfmarker} your north . They wanna go there . That 's what they say , is , " Where is it ? " . Where is that damn thing ?
Grad E: And the parser would output {disfmarker}
Grad B: Well , that 's a {disfmarker} a question mark . sh A lot of parsers , um , just , uh {disfmarker} That 's way beyond their scope , is {disfmarker} of interpreting that . You know ? But um , still outcome w the outcome will be some form of structure , with the town hall and maybe saying it 's a WH focus on the town hall . But to interpret it ,
Grad D: Mm - hmm .
Grad B: you know ? somebody else has to do that job later .
Professor C: Yeah .
Grad E: I 'm just trying to figure out what the SmartKom system would output , depending on these things .
Grad B: Um , it will probably tell you how far away it is , at least that 's {disfmarker} That 's even what Deep Map does . It tells you how far away it is , and {disfmarker} and shows it to you on a map . Because i we can not differentiate , at the moment , between , you know , the intention of wanting to go there or the intention of just know wanting to know where {disfmarker} where it is .
Grad D: People no might not be able to infer that either , right ? Like the fact {disfmarker} Like , I could imagine if someone came up to me and asked , " Where 's the city hall ? " , I might say , g ar " Are you trying to get there ? " Because how I describe um , t its location {disfmarker} uh , p probably depend on whether I think I should give them , you know , directions now , or say , you know , whatever , " It 's half a mile away " or something like that .
Grad B: Mm - hmm .  It 's a granularity factor ,
Professor C: Yeah .
Grad B: because where people ask you , " Where is New York ? " , you will tell them it 's on the East Coast .
Grad D: Uh - huh . Yeah . Exactly . Right . Right .
Grad B: Y y eh {disfmarker} you won't tell them how to get there , ft you know , take that bus to the airport and blah - blah - blah .
Grad D: Yeah .
Grad B: But if it 's the post office , you will tell them how to get there .
Grad D: Right . Mm - hmm .
Grad B: So th They have done some interesting experiments on that in Hamburg as well .
Grad D: Right .
Grad B: So .
Grad D: Right .
Professor C: But {disfmarker} i Go {disfmarker} go back to the {disfmarker} the uh , th
Grad B: So I w this is {disfmarker} " onto " is {disfmarker} is knowledge about buildings ,
Professor C: Yeah , that slide .
Grad B: their opening times , and then t coupled with time of day , um , this should {disfmarker} You know .
Grad D: So that context was like , um , their presumed purpose context , i like business or travel , as well as the utterance context , like , " I 'm now standing at this place at this time " .
Professor C: Yeah , well I think we ought to d a As we have all along , d We {disfmarker} we 've been distu distinguishing between situational context , which is what you have as context , and discourse context ,
Grad B: Mm - hmm .
Professor C: which you have as DH ,
Grad B: Nuh .
Professor C: I don't know what the H means .
Grad B: History . Discourse history . Yeah .
Professor C: OK . Whatever . So we can work out terminology later .
Grad B: Yep .
Professor C: So , they 're {disfmarker} they 're quite distinct . I mean , you need them both , but they 're quite distinct . And , so what we were talking about doing , a a as a first shot , is not doing any of the linguistics . Except to find out what seems to be {pause} useful . So , the {disfmarker} the {disfmarker} the reason the belief - net is in blue , is the notion would be {disfmarker} Uh , this may be a bad dis bad idea , but the idea is to take as a first goal , see if we could actually build a belief - net that would make this three way distinction uh , in a plausible way , given these {disfmarker} We have all these transcripts and we 're able to , by hand , extract the features to put in the belief - net . Saying , " Aha ! here 're the things which , if you get them out of {disfmarker} out of the language and discourse , and put them into the belief - net , it would tell you which of these three uh , intentions is most likely . " And if {disfmarker} to actually do that , build it , um {disfmarker} you know , run it {disfmarker} y y run it on the data where you hand - transcribe the parameters . And see how that goes . If that goes well , then we can start worrying about how we would extract them . So {disfmarker} where would you get this information ? And , expand it to {disfmarker} to other things like this . But if we can't do that , then we 're in trouble . I mean th th i i if you can't do this task , um {disfmarker}
Grad B: We need a different , uh , engine . Machine , I mean .
Professor C: Uh , uh , yeah , or something . Well it {disfmarker} i I if it {disfmarker} if it 's the belief - nets , we we 'll switch to you know , logic or some terrible thing , but I don't think that 's gonna be the case . I think that , uh , if we can get the information , a belief - net is a perfectly good way of doing the inferential combination of it . The real issue is , do what are the factors involved in determining this ? And I don't know .
Grad B: Hmm . But , only w
Professor C: Hold on a s Hold on a second .
Grad B: Muh .
Professor C: So , I know . Uh , uh , is it clear what 's going on here ?
Grad F: Yep .
Grad D: Um , I missed the beginning , but , um I guess {disfmarker} could you back to the slide , the previous one ? So , is it that it 's , um {disfmarker} These are all factors that uh , a These are the ones that you said that we are going to ignore now ? or that we want to {vocalsound} take into account ? You were saying n
Professor C: Take them into account . But {disfmarker} but you don't worry about {disfmarker} h
Grad D: Take the {disfmarker} the linguistic factors too . Oh , how to extract these features .
Professor C: how to extract them . So , f let 's find out which ones we need first ,
Grad D: OK . Got it .
Professor C: and {disfmarker}
Grad D: OK . And {disfmarker} and it 's clear from the data , um , like , sorta the correct answer in each case .
Professor C: No .
Grad D: But l
Grad B: No . But {disfmarker}
Grad D: OK .
Professor C: Let 's go back to th Let 's go back to the {disfmarker} the {disfmarker} the slide of data .
Grad D: That 's {disfmarker} that 's the thing I 'm curious ab
Grad B: Um {disfmarker}
Grad D: Like do we know from the data wh which {disfmarker} OK . So {disfmarker}
Grad B: Not from that data . But , um , since we are designing a {disfmarker} a {disfmarker} a {disfmarker} an , compared to this , even bigger data collection effort , {comment} um , we will definitely take care to put it in there ,
Grad D: Mm - hmm . Mm - hmm .
Grad B: in some shape , way , form over the other ,
Grad D: Mm - hmm .
Professor C: Yeah .
Grad B: to see whether we can , then , get sort of empirically validated data .
Grad D: Right .
Grad B: Um , from this , we can sometimes , you know {disfmarker} an and that 's {disfmarker} that {disfmarker} but that {disfmarker} isn't that what we need for a belief - net anyhow ? is sort of {disfmarker} s sometimes when people want to just see it , they phrase it more like this ? But it doesn't exclude anybody from phrasing it totally differently , even if they still {disfmarker}
Grad D: Mm - hmm . Right .
Grad B: you know ?
Grad D: Right .
Grad B: But then other factors may come into play that change the outcome of their belief - net . So , um , this is exactly what {disfmarker}
Grad D: Right .
Grad B: Because y you can never be sure . And I 'm sure even i the most , sort of , deliberate data collection experiment will never give you data that say , " Well , if it 's phrased like that , the intention is this . "
Grad D: Sure .
Grad B: You know , because then , uh , you {disfmarker}
Grad D: u u I mean , the only way you could get that is if you were to give th the x subjects a task . Right ? Where you have {disfmarker} where your , uh , current goal is to {disfmarker}
Grad B: We Yeah ! That 's what we 're doing .
Grad D: 
Grad B: But {disfmarker} but we will still get the phrasing all over the place .
Grad D: So that 's what you want ? OK . So you will know .
Grad B: I 'm sure that , you know {disfmarker}
Professor C: Yeah .
Grad D: Mm - hmm . The {disfmarker} No , that 's fine . I guess , it 's just knowing the intention from the experimental subject .
Professor C: Yeah .
Grad B: Mm - hmm .
Professor C: From that task , yeah . So , uh , I think you all know this , but we are going to actually use this little room
Grad D: 
Professor C: and start recording subjects probably within a month or something . So , this is not any {disfmarker} lo any of you guys ' worry , except that we may want to push that effort to get information we need . So our job {vocalsound} is to figure out how to solve these problems . If it turns out that we need data of a certain sort , then the sort of data collection branch can be , uh , asked to do that . And one of the reasons why we 're recording the meeting for these guys is cuz we want their help when we d we start doing uh , recording of subjects . So , yeah {disfmarker} y you 're absolutely right , though . No , you {disfmarker} you will not have , and there it is , and , uh {disfmarker} But you know , y y the , um {disfmarker}
Grad D: And I think the other concern that has come up before , too , is if it 's {disfmarker} um {disfmarker} I don't know if this was collected {disfmarker} what situation this data was collected in . Was it {disfmarker} is it the one that you showed in your talk ? Like people {disfmarker}
Grad B: No , no . No .
Grad D: But OK . So was this , like , someone actually mobile , like {disfmarker} s using a device ?
Grad B: Uh , N no , no not {disfmarker} i it was mobile but not {disfmarker} not with a w a real wizard system . So there were never answers .
Grad D: Uh - huh . OK . OK . But , is it {disfmarker} I guess I don't know {disfmarker} The situation of {disfmarker} of collecting th the data of , like {disfmarker} Here you could imagine them being {disfmarker} walking around the city . as like one situation . And then you have all sorts of other c situational context factors that would influence w how to interpret , like you said , the scope and things like that .
Grad B: Mm - hmm .
Grad D: If they 're doing it in a {disfmarker} you know , " I 'm sitting here with a map and asking questions " , I {disfmarker} I would imagine that the data would be really different . Um , so it 's just {disfmarker}
Grad B: Yeah . But {disfmarker} It was never th th the goal of that data collection to {disfmarker} to serve for sat for such a purpose . So that 's why for example the tasks were not differentiated by intentionality ,
Grad D: Mm - hmm .
Grad B: there was n there was no label ,
Grad D: Mm - hmm .
Grad B: you know , intention A , intention B , intention C .
Grad D: Right .
Grad B: Or task A , B , C . Um I 'm sure we can produce some if we need it , um , that {disfmarker} that will help us along those lines .
Grad D: Mm - hmm .
Grad B: But , you know , you gotta leave something for other people to model . So , to {disfmarker} Finding out what , you know , situational con what the contextual factors of the situation really are , you know is an interesting s interesting thing .
Grad D: Mm - hmm . Mm - hmm .
Grad B: u u Sort of I 'm , at the moment , curious and I 'm {disfmarker} I 'm {disfmarker} s w want to approach it from the end where we can s sort of start with this toy system that we can play around with ,
Grad D: Mm - hmm .
Grad B: so that we get a clearer notion of what input we need for that ,
Grad D: Mm - hmm .
Grad B: what suffices and what doesn't . And then we can start worrying about where to get this input , what {disfmarker} what do we need , you know {disfmarker} Ultimately once we are all experts in changing that parser , for example , maybe , there 's just a couple three things we need to do and then we get more whatever , part of speech and more construction - type - like stuff out of it .
Grad D: Mm - hmm . Hmm .
Grad B: It 's a m pragmatic approach , uh , at the moment .
Grad E: How exactly does the data collection work ? Do they have a map , and then you give them a scenario of some sort ?
Grad B: OK . Imagine you 're the {disfmarker} the subject . You 're gonna be in here , and somebody {disfmarker} And {disfmarker} and you see , uh , either th the three - D model , or uh , a QuickTime animation of standing u in a square in Heidelberg . So you actually see that . Um . The uh , um , first thing is you have to read a text about Heidelberg . So , just off a textbook , uh , tourist guide , to familiarize , uh , yourself with that sort of odd - sounding German street names , like Fischergasse and so forth . So that 's part one . Part two is , you 're told that this huge new , wonderful computer system exists , that can y tell you everything you want to know , and it understands you completely . And so you 're gonna pick up that phone , dial a number , and you get a certain amount of tasks that you have to solve . First you have to know {disfmarker} find out how to get to that place , maybe with the intention of buying stamps in there . Maybe {disfmarker} So , the next task is to get to a certain place and take a picture for your grandchild . The third one is to get information on the history of an object . The fourth one {disfmarker} And then the g system breaks down . It crashes , And {disfmarker}
Grad D: a At the third ? Right then ?
Grad B: After the third task .
Grad D: OK .
Grad B: And then {disfmarker} Or after the fourth . Some find {disfmarker} @ @ {comment} Forget that for now . And then , a human operator comes on , and {disfmarker} and exp apologizes that the system has crashed , but , you know , urges you to continue , you know ? now with a human operator . And so , you have basically the same tasks again , just with different objects , and you go through it again , and that was it . Oh , and one {disfmarker} one little bit {disfmarker} w And uh , the computer you are {disfmarker} you are being told the computer system knows exactly where you are , via GPS . When the human operator comes on , um , that person does not know . So the GPS is crashed as well . So the person first has to ask you " Where are you ? " . And so you have to do some {disfmarker} s tell the person sort of where you are , depending on what you see there . Um , this is a {disfmarker} a {disfmarker} a {disfmarker} a {disfmarker} a bit that I d I don't think we {disfmarker} Did we discuss that bit ? Uh , I just sort of squeezed that in now . But it 's something , uh , that would provide some very interesting data for some people I know . So .
Grad D: So , in the display you can {disfmarker} Oh , you said that you cou you might have a display that shows , like , the {disfmarker}
Grad B: Yeah . a Additionally , y you have a {disfmarker} a {disfmarker} a sort of a map type display .
Grad D: a w your perspective ? sort of ?
Grad B: Uh , two - D .
Grad D: And so , as you {disfmarker}
Grad B: n
Grad D: Oh , two - D . OK .
Grad B: Two - D .
Grad D: So as you move through it that 's - they just track it on the {disfmarker} for themselves
Grad B: Yeah . b y You don't {disfmarker} That 's {disfmarker}
Grad D: there .
Grad B: I don't know . I but y I don't think you really move , sort of .
Grad D: OK . So
Grad B: Yeah ? I mean that would be an {disfmarker} an {disfmarker} an enormous technical effort , unless we would {disfmarker} We can show it walks to , you know . We can have movies of walking , you walking through {disfmarker} through Heidelberg , and u ultimately arriving there .
Grad D: Mm - hmm .
Grad B: Maybe we wanna do that . Yeah .
Grad D: Uh , I was just trying to figure out how {disfmarker} how ambitious the system is .
Grad B: The map was sort of intended to {disfmarker} You want to go to that place . You know , and it 's sort of there .
Grad D: Mm - hmm .
Grad B: And you see the label of the name {disfmarker} So we get those names , pronunciation stuff , and so forth , and we can change that .
Grad D: Mm - hmm . Mm - hmm . So your tasks don't require you to {disfmarker} I mean , uh {disfmarker} yo you 're told {disfmarker} So when your task is , I don't know , " Go buy stamps " or something like that ? So , do you have to respond ? or does your {disfmarker} Uh , what are you ste what are you supposed to be telling the system ? Like , w what you 're doing now ? or {disfmarker}
Grad B: Well , we 'll see what people do .
Grad D: There 's no {disfmarker} OK , so it 's just like , " Let 's figure out what they would say under the circumstances " .
Grad B: Yeah , and {disfmarker} and we will record both sides . I mean , we will record the Wi - the Wizard {disfmarker}
Grad D: Uh - huh .
Grad B: I mean , in both cases it 's gonna be a human , in the computer , and in the operator case .
Grad D: Uh - huh .
Grad B: And we will re there will be some dialogue , you know ? So , you first have to do this , and that ,
Grad D: Yep .
Grad B: and {disfmarker} and {disfmarker}
Grad D: Mm - hmm .
Grad B: see wh what they say . We can ins instruct the , uh , wizard in how expressive and talkative he should be . But um , maybe the {disfmarker} maybe what you 're suggesting {disfmarker} Is what you 're suggesting that it might be too poor , the data , if we sort of limit it to this ping pong one t uh , task results in a question and then there 's an answer and that 's the end of the task ? You wanna m have it more {disfmarker} more steps , sort of ?
Grad D: Yeah , I {disfmarker} I don't know how much direction is given to the subject about what their interaction {disfmarker} I mean , th they 're unfamiliar w with interacting with the system .
Grad B: Mm - hmm .
Grad D: All they know is it 's this great system that could do s stuff .
Grad B: Mm - hmm .
Professor C: Oh yeah , but {disfmarker} to some extent this is a different discussion .
Grad D: Right ? So {disfmarker}
Professor C: OK ? So . Uh , we {disfmarker} we have to have this discussion of th the experiment , and the data collection , and all that sorta stuff
Grad D: Uh - huh .
Professor C: and we do have , um , a student who is a candidate for wizard . Uh , she 's gonna get in touch with me . It 's a student of Eve 's . FEY , Fey ? Spelled FEY . Do you {disfmarker} do you {disfmarker}
Grad D: Oh , Fey Parrill .
Professor C: You know her ?
Grad D: Yeah . Uh - huh .
Professor C: OK . Sh - Is sh
Grad D: She started taking the class last year and then didn't {disfmarker} um , you know , didn't continue . I g She 's a g
Professor C: She 's graduated .
Grad D: Is she an undergradua She is a graduate , OK .
Professor C: Yeah .
Grad D: Yeah , I m I know her very , very briefly . I know she was inter you know , interested in aspect and stuff like that .
Professor C: OK . So , anyway , she 's looking for some more part time work w while she 's waiting actually for graduate school . And she 'll be in touch . So we may have someone , uh , to do this , and she 's got you know , some background in {disfmarker} in all this stuff . And is a linguist st and , so So . {vocalsound} That 's {disfmarker} So , Nancy , we 'll have an At some point we 'll have another discussion on exactly wha t t you know , how that 's gonna go .
Grad D: Mm - hmm .
Professor C: And um , Jane , but also , uh , Liz have offered to help us do this , uh , data collection and design and stuff .
Grad D: Mm - hmm . Mmm .
Professor C: So , when we get to that we 'll have some people doing it that know what they 're doing .
Grad D: OK . I guess the reason I was asking about the sort of the de the details of this kind of thing is that , um , it 's one thing to collect data for , I don't know , speech recognition or various other tasks that have pretty c clear correct answers , but with intention {vocalsound} um , obviously , as you point out , there 's a lot of di other factors and {disfmarker} I 'm not really sure , um , how {disfmarker} how {disfmarker} e the question of how to make it a t appropriate toy version of that {disfmarker} Um , it 's ju it 's just hard . So , I mean , obviously it 's a {disfmarker}
Grad E: Yeah , uh , actually I guess that was my question . Is the intention implicit in the scenario that 's given ? Like , do the {disfmarker}
Grad D: It is , if they have these tasks that they 're supposed to {disfmarker}
Grad E: Yeah , I just wasn't sure to what level of detail the task was .
Grad D: to {disfmarker} to give {disfmarker} Yeah ,
Grad B: Mm - hmm .
Grad D: uh {disfmarker}
Grad B: n No one is , at the moment .
Grad D: Right . Right .
Grad E: OK .
Professor C: So , we that 's part of what we 'll have to figure out .
Grad D: Right .
Professor C: But , uh ,
Grad D: Mm - hmm .
Professor C: the {disfmarker} The problem that I was tr gonna try to focus on today was , let 's suppose by magic you could collect dialogues in which , one way or the other , you were able to , uh , figure out both the intention , and set the context , and know what language was used . So let 's suppose that we can get that kind of data . Um . The issue is , can we find a way to , basically , featurize it so that we get some discrete number of features so that , uh , when we know the values to all those features , or as many as possible , we can w come up with the best estimate of which of the , in this case three little intentions , are most likely .
Grad D: w What are the t three intentions ? Is it to go there , to see it , and {disfmarker}
Grad B: To come as close as possible to it .
Professor C: Th - the terminology we 're using is to {disfmarker}
Grad D: Yeah , it 's @ @ .
Professor C: Go back . To v
Grad D: OK .
Professor C: to View it . OK ? To Enter it . Now those {disfmarker} It seems to me those are cl you c you have no trouble with those being distinct . " Take a picture of it " you {disfmarker} you might well want to be a really rather different place than entering it .
Grad D: Mm - hmm . Mm - hmm .
Professor C: And , for an object that 's at all big , uh , sort of getting to the nearest part of it uh , could be quite different than either of those .
Grad D: Mm - hmm . Mm - hmm . Mm - hmm .
Professor C: Just sort of {disfmarker}
Grad D: OK , so now I understand the referent of Tango mode . I didn't get that before .
Grad E: See , I would have thought it was more of a waltz .
Grad B: S To " Waltz " it ?
Grad D: Yeah , like , how close are you gonna be ?
Professor C: Well .
Grad D: Like , {vocalsound} Tango 's really close .
Grad E: Yeah , cuz a tango {disfmarker} Yeah .
Professor C: Well , anyway . So {disfmarker}
Grad F: All these So , like , the question is how what features can {disfmarker} like , do you wanna try to extract from , say , the parse or whatever ?
Professor C: Right .
Grad F: Like , the presence of a word or the presence of a certain uh , stem , or {disfmarker} certain construction or whatever .
Professor C: Right . Is there a construction , or the kind of object , or w uh , anything else that 's in the si It 's either in the {disfmarker} in the s the discourse itself or in the context . So if it turns out that , whatever it is , you want to know whether the person 's uh , a tourist or not , OK ? that becomes a feature . Now , how you determine that is another issue . But fo for the current problem , it would just be , " OK , if you can be sure that it 's a tourist , versus a businessman , versus a native , " or something , uh , that would give you a lot of discriminatory power and then just have a little section in your belief - net that said , " pppt ! " Though sin f in the short run , you 'd set them ,
Grad F: Mm - hmm .
Professor C: and see ho how it worked , and then in the longer run , you would figure out how you could derive them . From previous discourse or w any anything else you knew .
Grad F: Right . So , how should {disfmarker} What 's the uh , plan ? Like , how should we go about figuring out these {disfmarker}
Professor C: OK . So , first of all is , uh , do e either of you guys , you got a favorite belief - net that you 've , you know , played with ? JavaBayes or something ?
Grad F: Oh . No , not really .
Professor C: OK . Well , anyway . f Get one . OK ? So {disfmarker} y so one of th one of the things we wanna do is actually , uh , pick a package , doesn't matter which one , uh , presumably one that 's got good interactive abilities , cuz a lot of what we 're gonna be d You know , we don't need the one that 'll solve massive , uh , belief - nets quickly . d w These are not gonna get big in {disfmarker} in the foreseeable future . But we do want one in which it 's easy to interact with and , uh , modify . Because i that 's {disfmarker} A lot of what it 's gonna be , is , um , playing with this . And probably one in which it 's easy to have , um , what amounts to transcript files . So that if {disfmarker} if we have all these cases {disfmarker} OK ? So we make up cases that have these features , OK , and then you 'd like to be able to say , " OK , here 's a bunch of cases " {disfmarker} There 're even ones tha that you can do learning OK ? So you have all their cases and {disfmarker} and their results and you have a {disfmarker} algorithms to go through and run around trying to set the {disfmarker} the probabilities for you . Um , probably that 's not worth it . I mean , my guess is we aren't gonna have enough data that 's good enough to make the {disfmarker} these data fitting ones worth it , but I don't know . So I would say you guy the first task for you two guys is to um , pick a package . OK , and you wanna it s You know , the standard things you want it stable , you want it {disfmarker} yeah , @ @ . And , as soon as we have one , we can start trying to , uh , make a first cut at what 's going on .
Grad B: An - Nuh .
Professor C: But it {disfmarker} what I like about it is it 's very concrete . OK ? We {disfmarker} we have a {disfmarker} we know what the outcomes are gonna be , and we have some {disfmarker} some data that 's loose , we can use our own intuition , and see how hard it is , and , importantly , what intermediate nodes we think we need . So it {disfmarker} if it turns out that just , thinking about the problem , you come up with things you really need to {disfmarker} You know , this is the kind of thing that is , you know , an intermediate little piece in your belief - net . That 'd be really interesting .
Grad E: Mm - hmm .
Grad B: And it {disfmarker} and it may serve as a platform for a person , maybe me , or whoever , who is interested in doing some linguistic analysis . I mean , w we have the For - FrameNet group here , and we can see what they have found out about those concepts already , that are contained in the data , um , you know , to come up with a nice little set of features and um , maybe even means of s uh , extracting them . And {disfmarker} and that altogether could also be {disfmarker} uh , become a nice paper that 's going to be published somewhere , if we sit down and write it . And um {disfmarker} When you said JavaBayes belief - net you were talking about ones that run on coffee ? or that are in the program language Java ?
Professor C: No , th It turns out that there is a , uh {disfmarker} The new end of Java libraries . OK , and it turns out one called
Grad B: Mmm . OK .
Professor C: Which is one that fair {disfmarker} people around here use a fair amount . I have no idea whether that 's {disfmarker}  The obvious advantage of that is that you can then , relatively easily , get all the other Java packages for GUIs or whatever else you might want to do .
Grad B: Mm - hmm .
Professor C: So that i that 's I think why a lot of people doing research use that . But it may not be {disfmarker} I have no idea whether that 's the best choice an and there 're plenty of people around , students in the department who , you know , live and breathe Bayes - nets . So , uh ,
Grad D: There 's the m tool kit that um , Kevin Murphy has developed ,
Professor C: Right . It 's OK .
Grad D: which might be useful too .
Grad F: Right .
Professor C: So , yeah , Kevin would be a good person to start with .
Grad D: And it 's available Matlab code .
Professor C: Nancy knows him well . I don't know I don't know whether you guys have met Kevin yet or not ,
Grad B: Mm - hmm .
Professor C: but , uh {disfmarker}
Grad F: Yeah , I know him .
Grad B: But i But since we all probably are pretty sure that , um , the {disfmarker}
Professor C: Yeah .
Grad B: For example , this th th the dialogue history is {disfmarker} is um , producing XML documents . M - three - L of course is XML . And the ontology that um , uh the student is {disfmarker} is constructing for me back in {disfmarker} in EML is in OIL and that 's also in XML . And so that 's where a lot of knowledge about bakeries , about hotels , about castles and stuff is gonna come from .
Professor C: Mm - hmm . Yeah .
Grad B: Um , so , if it has that IO capability and if it 's a Java package , it will definitely be able {disfmarker} We can couple .
Professor C: Yeah . So , yeah , we 're sort of {nonvocalsound} committed to XML as the kind of , uh , interchange . But that 's , you know , not a big deal .
Grad B: Who isn't , nuh ?
Professor C: So , in terms of {disfmarker} of {disfmarker} interchanging in and out of any module we build , It 'll be XML . And if you 're going off to queries to the ontology , for example , you 'll have to deal with its interface . But that 's {disfmarker} that 's fine an and um , all of these things have been built with much bigger projects than this in mind . So they {disfmarker} they have worked very hard . It 's kind of blackboards and multi - wave blackboards and ways of interchanging and registering your a And so forth . So , that I don't think is even worth us worrying about just yet . I mean if we can get the core of the thing to work , in a way that we 're comfortable with , then we ca we can get in and out of it with , uh , XML , um , little descriptors . I believe .
Grad B: Hmm . Yeah . Yeah , I like , for example , the {disfmarker} what you said about the getting input from {disfmarker} from just files about where you h where you have the data , have specified the features and so forth .
Professor C: I don't {disfmarker} I don't see {disfmarker}
Grad B: That 's , of course , easy also to do with , you know , XML .
Professor C: Uh , you could have an X {disfmarker} yeah , you could make and XML format for that . Sure .
Grad B: So r
Professor C: That {disfmarker} that {disfmarker} um , you know , feature value XML format is probably as good a way as any . So it 's als Yeah , I guess it 's also worth , um , while you 're poking around , poke around for XML packages that um , do things you 'd like .
Grad F: Doesn't {disfmarker} does SmartKom system have such packages ?
Grad B: Yeah .
Professor C: Sure .
Grad B: The {disfmarker} the lib M - three - L library does that . It 's also {disfmarker}
Professor C: And the question is , d you c you {disfmarker} you 'll have to l We 'll have to l That should be {disfmarker} ay We should be able to look at that {disfmarker}
Grad B: No , u u y um the {disfmarker} What I {disfmarker} What sort of came to my mind i is {disfmarker} was the notion of an idea that if {disfmarker} if there are l nets that can actually lear try to set their own , um , probability factors based on {disfmarker} on {disfmarker} on {disfmarker} on input {disfmarker}
Professor C: Yeah .
Grad B: which is in file format , if we , um , get really w wild on this , we may actually want to use some {disfmarker} some corpora that other people made and , for example , if {disfmarker} if they are in {disfmarker} in MATE , then we get X M L documents with discourse annotations , t you know , t from the discourse act down to the phonetic level .
Grad F: Mm - hmm .
Grad B: Um , Michael has a project where {disfmarker} you know , recognizing discourse acts and he does it all in MATE , and so they 're actually annotating data and data and data . So if we w if we think it 's worth it one of these days , not {disfmarker} not with this first prototype but maybe with a second , and we have the possibility of {disfmarker} of taking input that 's generated elsewhere and learn from that , that 'd be nice .
Grad F: Right .
Professor C: It 'd be nice , but {disfmarker} but I {disfmarker} I {disfmarker} I do I don't wanna count on it . I mean , you can't {disfmarker} you can't run your project based on the speculation that {disfmarker} that the data will come ,
Grad B: No , no , uh , just for {disfmarker}
Professor C: and you don't have to actually design the nets .
Grad B: Nuh . Just a back door that {disfmarker} I {disfmarker} I think we should devote m
Professor C: Could happen . Yeah . So in terms of {disfmarker} of the , um {disfmarker} the {disfmarker} what the SmartKom gives us for M - three - L packages , it could be that they 're fine , or it could be eeh . You don't {disfmarker} You know , you don't really like it . So we 're not {disfmarker} we 're not abs we 're not required to use their packages . We are required at the end to give them stuff in their format , but hey .
Grad F: Right .
Professor C: Um , it 's , uh {disfmarker} It doesn't control what you do in you know , internally .
Grad B: 
Grad E: What 's the time frame for this ?
Grad B: Two days ? Two , three days ?
Professor C: Huh ? Yeah bu w I 'd like that this {disfmarker} y yeah , this week , to ha to n to {vocalsound} have y guys , uh , you know , pick {vocalsound} the {disfmarker} y you know , belief - net package
Grad B: No .
Professor C: and tell us what it is , and give us a pointer so we can play with it or something .
Grad F: Sure .
Professor C: And , then as soon as we have it , I think we should start trying to populate it for this problem . Make a first cut at , you know , what 's going on , and probably the ea easiest way to do that is some on - line way . I mean , you can f figure out whether you wanna make it a web site or {disfmarker} You know , how
Grad B: Uh {disfmarker} I {disfmarker} I {disfmarker} I {disfmarker} um , OK , I {disfmarker} t Yeah . I was actually more joking . With the two or three days . So this was {disfmarker} was a usual jo
Professor C: OK , I wasn't .
Grad B: Um , it will take as long as y y yo you guys need for that .
Professor C: Yeah . Right .
Grad B: But um , maybe it might be interesting if {disfmarker} if the two of you can agree on who 's gonna be the speaker next Monday , to tell us something about the net you picked , and what it does , and how it does that .
Professor C: Well , y Well , or both of them speak .
Grad F: Sure .
Grad B: Yeah , or you can split it up .
Professor C: We don't care .
Grad B: So , y
Grad F: Hmm .
Grad B: So that will be sort of the assignment for next week , is to {disfmarker} to {disfmarker} for slides and whatever net you picked and what it can do and {disfmarker} and how far you 've gotten . Pppt !
Professor C: Well , I 'd like to also , though , uh , ha have a first cut at what the belief - net looks like . Even if it 's really crude . OK ? So , you know , here a here are {disfmarker}
Grad E: So we 're supposed to @ @ about features and whatnot ,
Professor C: Right . Yeah .
Grad E: and {disfmarker}
Grad F: Mm - hmm .
Professor C: And , as I said , what I 'd like to do is , I mean , what would be really great is you bring it in {disfmarker} If {disfmarker} if {disfmarker} if we could , uh , in the meeting , say , you know , " Here 's the package , here 's the current one we have , " uh , you know , " What other ideas do you have ? " and then we can think about this idea of making up the data file . Of , uh , you know , get a {disfmarker} t a p tentative format for it , let 's say XML , that says , l you know , " These are the various scenarios we 've experienced . " We can just add to that and there 'll be this {disfmarker} this file of them and when you think you 've got a better belief - net , You just run it against this , um {disfmarker} this data file .
Grad F: So we 'll be like , hand , uh , doing all the probabilities .
Professor C: Oh , yeah , unt until we know more .
Grad F: OK .
Grad E: And what 's the relation to this with {disfmarker} Changing the table so that the system works in English ?
Grad B: OK . So this is {disfmarker} Whi - while you were doing this , I received two lovely emails . The {disfmarker} the full NT and the full Linux version are there . I 've downloaded them both , and I started to unpack the Linux one {disfmarker} Uh , the NT one worked fine . and I started unta pack the Linux one , it told me that I can't really unpack it because it contains a future date . So this is the time difference between Germany . I had to wait until one o ' clock this afternoon before I was able to unpack it . Now , um {disfmarker} Then it will be my job to get this whole thing running both on Swede and on this machine . And so that we have it . And then um {disfmarker} Hopefully that {disfmarker} hoping that my urgent message will now come through to Ralph and Tilman that it will send some more documentation along , we {disfmarker} I control p Maybe that 's what I will do next Monday is show the state and show the system and show that .
Professor C: Yeah . Yeah . So the answer , Johno , is that these are , at the moment , separate . Uh , what one hopes is that when we understand how the analyzer works , we can both worry about converting it to English and worry about how it could ex extract the parameters we need for the belief - net .
Grad E: I guess my question was more about time frame . So we 're gonna do belief - nets this week , and then {disfmarker}
Professor C: Oh , yeah . I don't know . n None of this is i n Neither of these projects has got a real tight time - line , in the sense that over the next month there 's a {disfmarker} there 's a deliverable .
Grad E: OK .
Professor C: OK . S so uh , it 's opportu in that sense it 's opportunistic . If {disfmarker} if {disfmarker} you know , if we don't get any information for these guys f for several weeks then we aren't gonna sit around , you know , wasting time , trying to do the problem or guess what they {disfmarker} You know , just pppt ! go on and do other things .
Grad E: OK .
Grad B: Yeah , but uh {disfmarker} but the uh {disfmarker} This point is really {disfmarker} I think very , very valid that ultimately we hope that {disfmarker} that both will merge into a harmonious and , um , wonderful , um , state where we can not only do the bare necessities , IE , changing the table so it does exactly in English what it does in German , but also that we can sort of have the system where we can say , " OK , this is what it usually does , and now we add this little thing to it " , you know ? whatever , Johno 's and Bhaskara 's great belief - net , and we plug it in , and then for these certain tasks , and we know that navigational tasks are gonna be a core domain of the new system , it all {disfmarker} all of a sudden it does much better . Nuh ? Because it can produce better answers , tell the person , as I s showed you on this map , n you know , produce either you know , a red line that goes to the Vista point or a red line that goes to the Tango point or red line that goes to the door , which would be great . So not only can you show that you know something sensible but ultimately , if you produce a system like this , it takes the person where it wants to go . Rather than taking him always to the geometric center of a building ,
Grad F: Mmm .
Grad B: which is what they do now . And we even had to take out a bit . Nancy , you missed that part . We had to take out a bit of the road work . So that it doesn't take you to the wall {vocalsound} every time .
Grad D: Oh , really ?
Grad B: So . Um {disfmarker} So this was actually an actual problem that we encountered , which nobody have {disfmarker} has {disfmarker} because car navigation systems don't really care . You know , they get you to the beginning of the street , some now do the house number .
Grad D: Hmm .
Grad B: But even that is problematic .
Grad D: Mm - hmm .
Grad B: If you go d If you wanna drive to the SAP in Waldorf , I 'm sure the same is true of Microsoft , it takes you to the {disfmarker} the address , whatever , street number blah - blah - blah , you are miles away from the entrance .
Professor C: Yep .
Grad B: Because the s postal address is maybe a mailbox somewhere . Nuh ?
Grad D: Mm - hmm .
Grad B: but the entrance where you actually wanna go is somewhere completely different . So unless you 're a mail person you really don't wanna go there .
Grad D: Right , yeah .
Professor C: Probably not then , cuz y you probably can't drop the mail there anyway .
Grad B: Probably neither {disfmarker} e not even that .
Professor C: Yeah . Clear ?
Grad F: OK . Sounds good .
Grad E: The Powder - Tower is made of red limestone .
Grad D: I was wondering .
Grad B: Do you wanna see a picture ?
Grad D: OK . Sure !
Grad E: Sure !
Grad B: Have to reboot for that though .
Grad D: Um . So , you two , who 'll be working on this , li are {disfmarker} are you gl will you be doing {disfmarker} Well , I mean are you supposed to just do it by thinking about the situation ? Can you use the sample data ?
Professor C: Of course they use the sample data .
Grad D: Is it like {disfmarker} Yeah , I mean , ho is there more than {disfmarker} Is there a lot s of sample data that is beyond what you {disfmarker} what you have there ?
Grad B: There {disfmarker} there 's more than I showed , but um , um , I think this is sort of um , in part my job to look at that and {disfmarker} and to see whether there are features in there that can be extracted ,
Grad D: Yeah .
Grad B: and to come up with some features that are not you know , empirically based on {disfmarker} on a real experiment or on {disfmarker} on {disfmarker} on reality
Grad D: Right . Mm - hmm .
Grad B: but sort of on your intuition of you know , " Aha ! This is maybe a sign for that ,
Grad D: Mm - hmm .
Grad F: Mm - hmm .
Grad B: and this is maybe a sign for this . "
Grad D: Mm - hmm .
Grad E: Mm - hmm .
Grad F: So , yeah . Later this week we should sort of get together , and sort of start thinking about that , hopefully .
Grad B: Talk features . Yep .
Professor C: OK . We can end the meeting and call Adam , and then we wanna s look at some filthy pictures of Heidelberg . We can do that as well .
Grad B: Well they had {disfmarker} they used the ammunition {disfmarker} They stored the ammunition in that tower .
Professor C: Uh , is that OK ?
Grad B: And that 's why , when it was hit by uh , a cannon ball , it exploded .
Professor C: Alright . Oh . Ni
Grad E: It exploded . That 's why they call it the Powder - Tower .
Grad B: Ahh .
Grad E: OK . I first thought it had something to do with the material that it {disfmarker} w that 's why I asked .
Grad D: That 's right , OK .
Grad B: Mmm .
C began by asking some questions about the recording procedure and wanted to know what excising utterances from the transcript would look like. C asked several questions to that end but clarified that the source of inquiry was curiosity and nothing more. Additionally, C wanted A to explain to the meeting participants what the experiment was for.