Metiria Turei's AGM admission has exposed the inequality, racism and meanness that thrives in New Zealand society.
There are few people who can look back at themselves as young adults (18-24 years) without remembering past decisions and actions that we wish could be replayed with our current knowledge and experience. Times when we badly mismanaged relationships; broke the law and got away with it (or not); impulsively squandered money or said or did something stupid while intoxicated.
We also know now that no matter how intelligent or educated a young adult is, their brain isn't yet fully developed until the age of 25. Until then impulsiveness and risk taking are common features of any early adults' behaviour. The internationally respected Dunedin Study revealed that more than 90% of young males will break the law in some way. Most stop their criminal behaviour, settle down and become responsible citizens who contribute positively to society and the economy.
However, this transition into adulthood can be very different depending on socio-economic background. Our society and justice system treats the risk and law breaking phase of young adults very differently depending on an individual's social status. Maori and beneficiaries are regarded as the bottom of the heap and Maori beneficiaries are even lower. A post I wrote some years ago about the racism that exists in our criminal justice system is still valid as the statistics remain the same. We still lock up young Maori disproportionately and destroy their ability to succeed later in life because a criminal record will substantially reduce options.
My Pakeha experience as a student and young adult, and that of my children, is one of privilege. A stable home life, financial support and having educated parents usually creates a helpful pathway to success. It is a much harder road for many young Maori. Unemployment, poverty, poor housing and few resources to support academic endeavours can be insurmountable barriers.
Metiria Turei is statistical outlier for young Maori women. Despite Metiria's childhood being dominated by hardship and her early adulthood involving sole parent responsibilities, she was able to gain a law degree and became a co-leader of the Green Party by the age of 39. Such achievements for Maori (who generally lack financial support from their families) are only really possible with state support. Paula Bennett's removal of the training allowance for solo mums effectively kicked away the ladder used by both Paula and Metiria to climb out of the low waged economy.
Most offending by young adults is hedonsitic stuff involving alcohol, drugs or thrill seeking, but Metiria's dishonesty was motivated by her responsibilities as a mother and loyalty to a friend. While there is no disputing that her actions were unlawful, they are considered at the minor end of offending and there was little profit to herself. It is also interesting to note that few politicians have come out strongly condemning Metiria. Given the intense scrutiny of her 25 year old offences I'm sure many would rather not have that same attention brought to their own early lives.
The strident attacks on Metiria are mainly from the right-wing media, who flock like sharks at the sight of left-wing blood (Todd Barclay has got off lightly in comparison from main stream media). I find the framing and intensity of the attacks, for historical offences that have been openly admitted, bordering on the hysterical.
More recent behaviours of National Government Ministers have been far more questionable in terms of morality and fiscal impact. Murray McCully's pathetic Saudi bribe cost the country $11.5 million and he was never held to account for his lies over this dodgy deal. The previous Prime Minister's support of New Zealand's tax haven status was also a saga full of conflicts of interest and lies at a level that should have brought down the government, if only it had the media scrutiny it deserved. Knowingly allowing money laundering and tax evasion, through a lightly regulated investment regime (involving billions of dollars,) makes Metiria's dishonesty pale in comparison.
For the Right, Metiria still represents everything they despise. To them she is still a young Maori solo mum living off 'good honest taxpayers' while promoting cannabis and poking fun at our sacred institutions. Stories of her heinous crimes are accompanied by shots of a dreadlocked Metiria with her baby, people smoking huge joints, and McGillicuddies dancing in kilts. The fact that she has worked as a commercial lawyer, is a Co-leader of New Zealand's third largest political party and has been a hard working and respected MP for fifteen years has been shamefully ignored.
Major fraudster and bully, Joanne Harrison, has had lighter media treatment than Metiria, which only the supports the evidence that white collar crime is considered a lesser evil than benefit fraud.
Personal attacks and abuse are nothing new for Metiria. She was cruelly criticised for wearing designer clothes (when such challenges would never be made to a Pakeha MP) and she generated much anger by daring to question PM John Key's ignorant view that the colonisation of New Zealand was nonviolent. The underlying racism is clear.
Metiria Turei has again spoken truth to power and slapped Pakeha privilege in the face. While Mike Hosking and Patrick Gower bristle with indignation from their right-wing media pulpits, there is a steadily growing crowd (from the marginalised and disenfranchised many) coming out of the shadows to stand behind Metiria. At last there is someone in Parliament who has walked in their shoes (and who still remembers) and is openly admitting what they never can.
And yet again the Greens show their died in the wool racism, lack of ethics and morals.
So MT being Maori makes what she did ok?
The truth of the matter is yet to come out. So far we have two helpings of fraud - social welfare and electoral. How do you know she has changed and not continuing her fraudulent ways on the Greens? She clearly had family support in raising her baby - she has admitted that. What else has she not told her ardent supporters.
She's clearly not what she's saying she is.
Paranormal: Surely family support is a good thing and it is not always a fianacial support. Family support is about being there, being supportive, loving, encouraging,helpful, interested and being present family support is not a crime. I am saddened and shocked by the vitriolic attacks on Metiria Turei. How easy it is to see the splinter in our sisters eye amd not the plank in our own. To borrow a phrase from the book so many of her self righteous attackers supposedly follow.
Paranormal, you totally don't get it. Breaking the law is not OK, but the fact that so many young mothers feel forced to is the real issue. What young sole mothers need are wrap around services that make them feel supported and able to become independent of the state through maximising their potential. The current system is prying, humiliating, punitive and judgemental and actually traps too many into a life of poverty.
There are so many reasons for young woman ending up in those circumstances but the vitriol directed at them by our wider society is pretty appalling. Bringing up children on a benefit is rarely a choice and one of the hardest things for anyone to do. Sadly around 25% of our young children suffer under this regime as well.
And look how you both frame up legitimate questioning of MT's probity.
MT has stated how she has profited from gaming the system and the support she received from the father and his family. This is not the story of poverty you are desperately trying to make it.
My own family has a similar story. A family member who decided to make a welfare driven lifestyle choice rather than working to achieve her goals. That is where the problem in society lays, not your alleged lack of 'compassion'.
Your brand of 'compassion' (tm) leads to welfare dependency, poverty, and crime. It is you who should hang your heads in shame suggesting that welfare leads to good outcomes where it plainly doesn't. There are now three generations of our family that are impacted by a poor welfare lifestyle decision.
Paranormal, I would love to hear from you what Metiria gained from her dishonesty. I gather she was able to feed her daughter better and get a law degree. Most of Metiria's life has been devoted to the service of others and one could hardly say that she has ever lived a life of luxury. I would say that the state has had a good return from its investment and Metiria has paid for it in her service many times over. What she gained illegally pales against the 'legal' allowances and benefits that the likes of Bill English and Todd Barclay have received as MPs. Remember it was Metiria who made MP expenses public knowledge and has probably saved the tax payer millions. I have noted that some of the largest expense claims come from back bench National MPs with little responsibility. The sense of entitlement from some is disgusting.
You must know that very few young mothers ever remain on a benefit for very long. It is not welfare that traps people into ongoing lives of dependency, it is the lack of real support. Living lives in substandard housing, continually eating cheap crap food and forced to work in jobs paying beneath a living wage is the real trap. This Government believes that if you punish people for being poor and forcing them and their children to live in crowded homes, caravans, garages and cars then they should just aspire for better. However beating people with draconian welfare sticks just breaks vulnerable people and few see themselves achieving more.
What is wrong with giving struggling families decent warm homes and useful support that enables them to gain useful skills and qualifications. The best way to lift people out of a life of dependency is to provide practical support, education and build self-worth.
I have been door knocking as part of the campaign and I have visited home after home, in substandard condition, where young parents are doing their best to give their kids a good life but finding it really, really hard.
You should hang your head in shame to believe that welfare should be like a prison sentence, a punishment so bad that no one wants to go back there. That is the system that has given us one of the largest prison populations per capita in the world and the worst outcomes for child health and welfare.
Nine years of National's welfare management has not been good for families and children and we can do better.
You go Teresa. The fact you couldn't be more wrong is irrelevant for you isn't it?
BS, interesting that you rightly blame our high prison population on welfare, and yet you think more of it (welfare) is the solution.
I see you have not even seen the Electoral Commission's statement about what you consider to be a fraudulent action by Metiria @Paranormal, because if you had then you'd know that many young people do it; even your beloved John & Bronagh Key are enrolled at an address they don't even live at. However given your myopic attitude I wouldn't be surprised if you're well informed. Another point is that there are many people on welfare because they are legitimately unable to work due to illness, injury, or disease and unfortunately for them they're forced through hoops for a pittance of support. It comes as no surprise that some are desperately forced into crime because they're unable to make ends meet. However there are politicians and voters such as yourself, who really should be indignant at the cruel injustices that the vulnerable in our society have to deal with but aren't. If you paid actual attention you'd also see that tax evasion is a much bigger deal, but is not nearly dealt with as stringently as what welfare fraud is, and is costing our country so much more, the people who are convicted of tax evasion can even get off from repaying the debt if it's proven that they'd enter financial hardship far less than what a welfare defrauder experiences. I don't see you expressing any outrage at National, even with John Key's 11 million dollars of tax payers money being donated to the Clinton foundation. There are many matters committed by the politicians of other parties that are far worse than what Metiria has done yet no one is doing anything about it.
For the record I have never voted for or supported National. I was a Green voter until I worked out you guys are all about entrenching poverty and welfare dependency because that means long term sustainability for you.
The sort of vitriol heaped on an individual here for just pointing out the obvious shows you are clearly not ready for anything close to being in government. Diversity for you is all about homogeneity.
Paranormal, a major reason why we have a high prison population (costing $100,000 per prisoner per year) is because of under-investment in struggling families. You consider spending on welfare a drain of resources, I see it as the ambulance at the top of the cliff. While more money on benefits would help, we also need to boost the support we provide to those families that need it. Many of these struggling families come from the damaged people the state created through CYFs. The state abused (and continues to abuse through neglect) a good number of children in their care, then abandoned them at 17 to fend for themselves. A large percentage of them populate our prisons and form our dysfunctional families. You may wish to punish them further, I think they deserve decent support to right the wrongs done to them in their early lives.
Also not that the rises in child poverty tend to occur under national Governments. Ruth Richardson's Mother of All Budgets saw child poverty double and this government has facilitated something similar. Concerningly there has been a refusal to assess child poverty as other countries do now, so we can't make a comparison.
BS you also missed the fact that the majority of the prison population are illiterate-and that's after spending time in our one size fits all education system that also failed them.
I'm not a National supporter but the one thing they have done well is the dramatic plunge in the teenage pregnancy rate. That's a whole generation of children that will not have to live through the deprivation you want them to endure trapped in welfare dependency. That you want to turn this around will cost the Greens votes in well off suburban homes with teenage daughters.
Teresa's comment was indeed abuse and indicative of her intolerance. Robust debate is something Green supporters don't seem to understand.
Paranormal, you make huge assumptions. Research shows that socio-economic background makes the biggest difference to academic achievement. Only 10% of a child's time is spent in school the other 90% of their time has the biggest influence. Remember that NZs ranking in education has plummeted almost 20 places in the 9 years under National.
I have commented on a number of National leaning blogs and have found them very closed minded and you make big assumptions that all who comment here are Green supporters.
At least she wasn't done for living in a relationship, something Paula Bennett is being investigated for, as many years later she is now married to the same man she is accused of being in a relationship with whilst receiving DPB. You, Paranormal, are closed minded and bigoted!You hide behind a fake facade, an assumed alias and its sad to see that narrow mindedness is still so prevalent in NZ. Why isn't anyone demanding her blood, after all she is part Maori? Oh no wait a minute, she's mainly European right, that puts her well and truly above the Metirias of the world. Pfffft.
I lived in Australia till i was 19 and moved to New Zealand I had a everything I ever wanted and needed from the amazing support of my family. I becames a manager at Mcdonalds when I turned 18 was trained prior to that. With that said I came to New Zealand with awesome credentials Higher School Certificate, Certificate 3 in Business, 3 years experience of training and one year of management which I was groomed for since I was 16 at Mcdonalds. However that counted for absolutely nothing in this country, I was jobless for 9 months no support from WINZ my partner was heavily pregnant I was living with my inlaws. I found a job eventually with my business credentials and references that begged me not to leave aussie I became a forkhoist driver in a storage business Otahuhu (i lived in West Auckland) On minimum wage we were hardly in a position to raise a child.
I had applied for countless jobs signs out the front of stores in Westgate and Henderson said NOW HIRING - though when it came to the interviews or even speaking to the hiring manager it became clear that even hearing a maori voice put my job prospects in the drain and shame on those brand name stores.
Its been 6 years since then and I have worked in Auckland in different jobs and Wellington and now currently residing in Northland moerewa and all i have seen is the inequality of race not just maori and pakeha but all races. The judgement placed upon people because of colour and gender and how in these cities there are clear divides between the rich and the poor no longer is there a middle class.
What I have to deal with is facing the fact I may not be able to wake up for my kids, I dont get government support my extended family in Moerewa are barely making enough for themselves so they cant help me.
So I understand the need for some to try and survive for our families, what she has done for her party up until now counts little because she chose survival of her family in the past.
And to those that only have bad words towards Metiria, for those that also have bad words directing to her lineage and to those who have bad words for the greens. GROW UP, DEAL WITH IT AND INSTEAD OF ARGUING AND BICKERING AND HIGHLIGHTING THE BAD FOR PERSONAL OR PARTY GAINS FIX THE SYSTEM because there are to many poor out there and its only getting worse.
It is really wonderful to have lots of people turning up at our temporary Invercargill office wanting to volunteer because they totally get what Metiria was standing up for. The system doesn't work and too many families and children deserve better!
It gets boring hearing bout how hard maori have it, how bout the maori, tongans, samoans, chinese, and other cultures born n living here that are struggling just as much.....the fact that more maori are in jails can reflect there true nature. How about the mori ori who maori conquered wiped out the adult males and assymilated the women n kids into their ways and yet deny the mori ori were even a race before Maori. People black white or yellow are in dire need of help in all aspects. Put the racial manipulation aside and help each other out. Band together to rid the govt. People like the above named and paula bennit forget there beginnings. Good on turei for changing her ways of the past. People break the law all the time a vast majority but only a few go to jail.....breaking the law full stop makes you a criminal not going to jail. The Ngai Tahu iwi for example is richest iwi in nz but how many of the ordinary members still in poverty? Yet the iwi leaders are $100,000+ per year???
It happens in all cultures in nz not just maori the only difference is that you only hear about the maori side lets hear from the other sides.
Slaps white privilege more like aids white privilege.
Paul hartley the son Of Northshore Mayor Ann Hartley is babies daddy and she refused to name him on the birth cert because she didn't want him hassled by the IRD. Can we get child support from him please ?
PS just so you all know Ann hartley was part of the Labour lead govt that raised the penalties for women that didn't name the father of their child..
This is the very thing that Metiria was making a point about and yet you enthusiastically join all those who just don't get what is wrong with that invasion of privacy.
Our elderly get a universal basic income with no questions asked. No one asks an elderly women who she is sleeping with and if a sexual partner provides income support. The state doesn't want to know if an elderly person is getting any cash supplement on the side. There is no prying around how many nights a friend may stay over and whether they are in a sexual relationship. It would be considered an appalling invasion of privacy.
At one time all mothers received a child allowance with no questions asked to ensure all children would be fed and clothed. Now few of our elderly live in poverty and are more financially secure than those in any other developed country. On the other hand 28% of our children live in poverty and many are deprived of basic necessities. Our ranking for children's welfare is ranked at the bottom of the developed world.
Something is really wrong with our society!
No BS - something is seriously wrong with the Greens that you don't 'get it'. There's a complete lack of morals and ethics that the voting public are recognising.
The fact you Greens have castigated the two caucus members with morals who stood up and spoke truth to power is telling. It brings into sharp focus Green hypocrisy and quickly destroys the brand you've tried to build up over the last 30 years.
The scrutiny was brought about when MT threw her family under the bus by saying if she didn't rort the system her child would starve. That was a bare faced lie. The fathers family provided significant financial, emotional and practical support. How do you think they are feeling after what MT said about them by implication?
If you want to see how the Green brand is being destroyed look no further than Jackie's comment above. We don't know if Jackie is a Green, but she is posting in support of Greens on a Greens affiliated blog. Clearly she doesn't understand that calling me a bigot because of her assumptions of my ethnicity and political grouping actually shows her, and by association the Greens, as nasty bigots.
That's a significant point to note here - nothing I have said is about race, but you and a number of your commenters have wanted to make it all about race. So who are the real bigots?
"I had a great case worker at what we now call WINZ, who treated me with respect. I had the training incentive allowance as a grant to help me pay my fees and childcare. I had great support from my family and my baby's dad, and his family too."
Metiria has always admitted she had great support but it still wasn't enough. Remember this was just after Ruth Richardson's 25% cuts and Metiria was also using childcare before the free hours. How you can make such certainty about her actual financial situation is beyond me and a little worrying.
I have provided plenty of references and links in past posts to support the racism that does exist in our society and Metiria has suffered this many times as an opposition MP.
The two MP's were not castigate because of their belief but because the manner in which they tried to force a decision that went against the agreements they had signed and the party processes. Any other party would have done the same in similar circumstances.
Are you sure it's racism MT has suffered or just a reaction to her as an individual?
How can I be so sure about the family support she received - simple, the family have contacted the media to say they supported her to the tune of $20k per annum. This is at a time when the starting wage was about $7k. Why are the family contacting the media? It's because they are hurt by being thrown under the bus.
Sooo - Clendon and Graham are guilty of not 'following orders' rather than standing up for what they believe in and speaking truth to power? Have a look at what you are saying and try to actually think about what it means. Suggesting they were out of line for not following party processes when they were clearly following their consciences just makes you look even worse.
Why would I give a damn about National MP's? The Greens are supposed to hold themselves above that sort of behaviour, but as soon as you're found wanting you're not owning it but saying 'look over there, they're much worse than us'. Keep on going, you are just digging a deeper hole.
Oh dear, Paranormal, personalising politics is a dangerous road you seem keen to embrace. I note that you just loyally repeat rumour and spin and provide no links to support your charges. A little like National's claim that under Labour's water charging, cabbages will cost $18 each. So much nonsense.
The Greens are not an authoritarian party, no orders just principle and agreements and decisions following democratic process. Following ones conscience is fine but there are ways of acting on that too.
Keep defending the indefensible. Seems I'm not the only one that doesn't get the Green brand of 'democratic process'. Certainly looks authoritarian from the outside.
As for your 'loyally repeating spin'. I was repeating what long term Green friend John Campbell was accurately reporting on state radio. That's why the public have lost faith in the Greens. See if you can work out why.
You can imply I'm a liar if you like, Paranormal, but as a member of our executive I was part of the democratic process and I can tell you emphatically that we manage our party democratically and consult our with members for important decisions.
I'm sure we could have handled some stuff better but you have to admit that compared to the debacles and lies involving foreign trusts, bribing Saudi businessmen and manipulating housing allowances, the attacks on the Greens have been bizarrely extreme.


text:
\section{Introduction}
\label{sec:intro}

Character-level models have obtained impressive performance on a number of NLP tasks, ranging from the classic POS-tagging \citep{santos2014learning} to complex tasks such as Discourse Representation Structure (DRS) parsing \citep{drstacl:18}. However, this was before the large pretrained language models \citep{Peters:2018,bert:19} took over the field, with the consequence that for most NLP tasks, state-of-the-art performance is now obtained by fine-tuning on one of these models (e.g., \citealp{conneau-etal-2020-unsupervised}).

Does this mean that, despite a long tradition of being used in language-related tasks (see Section~\ref{sec:backgroundchar}), character-level representations are no longer useful? We try to answer this question by looking at semantic parsing, specifically DRS parsing \citep{eacl:pmb, pmb-LREC:18}. We aim to answer the following research questions:

\begin{enumerate}

\setlength\itemsep{-4pt}

   \item Do pretrained language models (LMs) outperform character-level models for DRS parsing?

    \item Can character and LM representations be combined to improve performance, and if so, what is the best method of combining them?

    \item How do these improvements compare to adding linguistic features?

    \item Are the improvements robust across different pretrained language models, languages, and data sets?

    \item On what type of sentences do character-level representations specifically help?
\end{enumerate}

\inlineheadernoquad{Why semantic parsing?} Semantic parsing is the task of automatically mapping natural language utterances to interpretable meaning representations. The produced meaning representations can then potentially be used to improve downstream NLP applications (e.g., \citealp{issa-etal-2018-abstract,song-etal-2019-semantic, mihaylov-frank-2019-discourse}), though the introduction of large pretrained language models has shown that explicit formal meaning representations might not be a necessary component to achieve high accuracy. However, it is now known that these models lack reasoning capabilities, often simply exploiting statistical artifacts in the data sets, instead of actually \textit{understanding} language \citep{niven-kao-2019-probing, mccoy-etal-2019-right}. Moreover, \citet{ettinger2020bert} found that the popular \textsc{bert}{} model \citep{bert:19} completely failed to acquire a general understanding of negation. Related, \citet{bender-koller-2020-climbing} contend that meaning cannot be learned from form alone, and argue for approaches that focus on grounding the language (communication) in the real world. We believe formal meaning representations therefore have an important role to play in future semantic applications, as semantic parsers produce an explicit model of a real-world interpretation.

\begin{figure*}[!t]
\captionsetup{singlelinecheck=false,justification=justified}
\begin{minipage}[t]{.25\linewidth}
\vspace{0pt}
\setlength{\tabcolsep}{2pt}
\centering
\footnotesize
\begin{tabular}{ll}
\toprule
\multicolumn{2}{l}{\small \textbf{Sent:} I haven't been to Boston since 2013.} \\
\midrule
\clause{\boxvar{b1} \operator{NEGATION} \boxvar{b2}}      &\clause{\boxvar{b3} \operator{REF} \disvar{x1}}    \\
\clause{\boxvar{b1} \operator{REF} \disvar{t1}}    &\clause{\boxvar{b3} \role{Name} \disvar{x1} "boston"}    \\
\clause{\boxvar{b1} \operator{TPR} \disvar{t1} "now"}  &   \clause{\boxvar{b3} \operator{PRESUPPOSITION} \boxvar{b2}}    \\
\clause{\boxvar{b1} \concept{time} "n.08" \disvar{t1}}  &  \clause{\boxvar{b3} \concept{city} "n.01" \disvar{x1}}    \\
\clause{\boxvar{b2} \operator{REF} \disvar{e1}}    &\clause{\boxvar{b2} \role{Start} \disvar{e1} \disvar{t2} }    \\
\clause{\boxvar{b2} \role{Theme} \disvar{e1} "speaker"}  &   \clause{\boxvar{b2} \operator{REF} \disvar{t2}}    \\
\clause{\boxvar{b2} \role{Time} \disvar{e1} \disvar{t1}}  &  \clause{\boxvar{b2} \concept{time "n.08"} \disvar{t2}}    \\
\clause{\boxvar{b2} \concept{be} "v.03" \disvar{e1}}    & \clause{\boxvar{b2} \role{Location} \disvar{e1} \disvar{x1}} \\
\multicolumn{2}{l}{\clause{\boxvar{b2} \role{YearOfCentury} \disvar{t2} "2013"}}  \\
\bottomrule
\end{tabular}
\end{minipage}%
\begin{minipage}[t]{.75\linewidth}
\vspace{0.01cm}
\centering
\includegraphics[width=0.66\textwidth,right]{smaller_drs_example.pdf}
\end{minipage}%

\vspace{-0.3cm}
\caption{\label{fig:clauses}\label{fig:drs}Example DRS in both clause (left) and box (right) representation.}
\vspace{-0.3cm}
\end{figure*}

\inlineheader{Why Discourse Representation Structures?} DRS parsing is a task that combines logical, pragmatic and lexical components of semantics in a single meaning representation. The task is complex and comprises other NLP tasks, such as semantic role labeling, word sense disambiguation, co-reference resolution and named entity tagging. Also, DRSs show explicit scope for certain operators, which allows for a more principled and linguistically motivated treatment of negation, modals and quantification, as has been advocated in formal semantics.
Moreover, DRSs can be translated to formal logic, which allows for automatic forms of inference by third parties. Lastly, annotated DRSs are available in four languages  (\citealp{eacl:pmb}, see Section~\ref{sec:data}), allowing us to evaluate our models on multiple languages.

\section{Background}
\label{sec:background}

\subsection{Character-level models}
\label{sec:backgroundchar}

The power of character-level representations has long been known in the field.
In earlier work, they were successfully used in a range of tasks, including
text-to-speech \citep{sejnowski1987parallel}, parallel text alignment \citep{church1993char_align}, grapheme to phoneme conversion \citep{kaplan:94}, language identification \citep{dunning1994statistical}, topical similarity prediction \citep{cavnar1995using}, named entity recognition \citep{klein2003named}, authorship attribution \citep{peng2003language} and statistical machine translation \citep{vilar2007can}.

More recently, they also proved useful as input representations for neural networks, starting with success in general language modelling \citep{sutskever:11,kim2016character,bojanowski2017enriching}, but also for a range of other tasks, including tokenization \citep{evang2013elephant}, POS-tagging \citep{santos2014learning, plank:16}, dependency parsing \citep{ballesteros-etal-2015-improved, vania-etal-2018-character} and neural machine translation \citep{chung-etal-2016-character,costa2016character, luong-manning-2016-achieving, cherry-etal-2018-revisiting}.

In semantic parsing, if character-level representations are employed, they are commonly used in combination with non-contextual word-level representations \citep{lewis-etal-2016-lstm, ballesteros2017amr,groschwitz-etal-2018-amr, cai-lam-2019-core}. There are a few recent studies that did use character-level representations in combination with \textsc{bert}{} \citep{zhang-etal-2019-amr,zhang-etal-2019-broad,cai-lam-2020-amr}, though only \citet{zhang-etal-2019-amr} provided an ablation score without the characters. Moreover, it is not clear if this small improvement was significant.
\citet{clinAMR:17} and \citet{drstacl:18}, on the other hand, used solely character-level representations in an end-to-end fashion, using a bi-LSTM sequence-to-sequence model, which outperformed word-based models that employed non-contextual embeddings.

\subsection{Discourse Representation Structures}
\label{sec:backgrounddrs}

DRSs are formal meaning representations introduced by Discourse Representation Theory \citep{kampreyle:drt} with the aim to capture the meaning of texts (Figure~\ref{fig:drs}).
Many variants of DRS have been proposed throughout the years.
We adopt
\citet{venhuizen2018discourse}'s version of DRT, which is close to Kamp's original ideas, but has
a neo-Davidsonian view of event semantics and explicitly represents presuppositions.

\inlineheader{Corpora} The Groningen Meaning Bank (GMB, \citealp{gmb:eacl,GMB:2017}) was the first attempt of annotating open domain English texts with DRSs. The released documents are partially corrected, but there are no gold standard sets available for evaluation. A similar corpus is the Parallel Meaning Bank (PMB, \citealp{eacl:pmb}), which builds upon the GMB in a number of ways. It contains (parallel) texts in four languages: English, German, Italian and Dutch, with more fine-grained and language-neutral DRSs. Semantic tags are used during annotation \citep{Bjervaetal:16, semantic-tagset:17}, and all non-logical DRS symbols are  grounded in either WordNet \citep{wordnet} or VerbNet \citep{Bonial:11}. Moreover, its releases contain gold standard DRSs. For these reasons, we take the PMB as our corpus of choice to evaluate our DRS parsers.

\inlineheader{DRS parsing} Early approaches to DRS parsing employed rule-based systems for small English texts \citep{johnsonklein, wadaasher, Bos2001ICOS}. The first open domain DRS parser is Boxer \citep{step2008:boxer, boxer}, which is a combination of rule-based and statistical models.
\citet{le:12} used a probabilistic parsing model that used dependency structures to parse GMB data as graphs. More recently, \citet{neural_drs_gmb:18} proposed a neural model that produces (tree-structured) DRSs in three steps by first learning the general (box) structure of a DRS, after which specific conditions and referents are filled in. In follow-up work \citep{liu-etal-2019-acl} they extend this work by adding an improved attention mechanism and constraining the decoder to ensure well-formed output. This model achieved impressive performance on both sentence-level and document-level DRS parsing on GMB data. \citet{fu2020drts} in turn improve on this work by employing a Graph Attention Network during both encoding and decoding.

The introduction of gold standard DRSs in the PMB enabled a principled comparison of approaches.
In our previous work \citep{drstacl:18}, we showed that sequence-to-sequence models can successfully learn to produce DRSs, with characters as the preferred representation. In follow-up work, we improved on these scores by adding linguistic features \citep{van-noord-etal-2019-linguistic}. The first shared task on DRS parsing \citep{abzianidze-etal-2019-first} sparked more interested in the topic, with a system based on stack-LSTMs \citep{evang-2019-transition} and a neural graph-based system \citep{fancellu-etal-2019-semantic}. The best system \citep{liu-etal-2019-discourse} used a similar approach as \citet{drstacl:18}, but swapped the bi-LSTM encoder for a Transformer. We will compare our approach to these models in Section~\ref{sec:results}.

\vspace{-0.1cm}
\section{Method}
\vspace{-0.1cm}

\subsection{Neural Architecture}
\label{sec:neural}

As our baseline system, we start from a fairly standard sequence-to-sequence model with attention \citep{bahdanau2015neural}, implemented in AllenNLP \cite{Gardner2017AllenNLP}.\footnote{\url{https://github.com/RikVN/allennlp}} We improve on this model in a number of ways, mainly based on Nematus \citep{sennrich-etal-2017-nematus}: (i) we initialize the decoder hidden state with the mean of all encoder states, (ii) we add an extra linear layer between this mean encoder state and the initial decoder state and (iii) we add an extra linear layer after each decoder state.

Specifically, given a source sequence $(s_1,\dots, s_l)$ of length $l$, and a target sequence $(t_1,\dots, t_k)$ of length $k$, let $\vect{e}_i$ be the embedding of source symbol $i$, let $\vect{h}_i$ be the encoder hidden state at source position $i$ and let $\vect{d}_j$ be the decoder state at target position $j$. A single forward encoder state is obtained as follows:  $\overrightarrow{\vect{h}}_i = \text{LSTM} (\overrightarrow{\vect{h}}_{i-1}, \vect{e}_i)$. The final state is obtained by concatenating the forward and backward hidden states,  $\vect{h}_i = [ \overrightarrow{\vect{h}}_i; \overleftarrow{\vect{h}}_i ]$. The decoder is initialized with the average over all encoder states:  $ \vect{c}_\mathrm{tok} = \left(\sum_{i=1}^{l}\vect{h}_i \right) / \: l$ and $\vect{d}_0 = \tanh \left( \matr{W}_\mathrm{init}\:  \vect{c}_\mathrm{tok} \right)$.

\inlineheader{Characters in one encoder} We will experiment with adding character-level information in either one or two encoders. For one encoder, we use char-CNN \citep{kim2016character}, which runs a Convolutional Neural Network \citep{lecuncnn:90} over the characters for each token. It applies convolution layers for certain \emph{widths}, which in essence select n-grams of characters. For each width, it does this a predefined number of times, referred to as the number of \emph{filters}. The filter vectors form a matrix, which is then pooled to a vector by taking the max value of each initial filter vector. A detailed schematic overview of this procedure is shown in Appendix~\ref{app:charcnn}. However, we usually do not look at only a single width, but at a range of widths, e.g., $\left[ 1, 2, 3, 4, 5 \right]$. In that case, we simply concatenate the resulting vectors to obtain our final char-CNN embedding: $\vect{e}_\mathrm{char_i} = \left[ \vect{e}_{w1} ; \vect{e}_{w2} ; \vect{e}_{w3} ; \vect{e}_{w4} ; \vect{e}_{w5} \right]$. Each width-filter combination has independent learnable parameters. Finally, the char-CNN embedding is concatenated to the token-level representation, which is fed to the encoder: $\vect{e}_i = [ \vect{e}_\mathrm{tok_i}; \vect{e}_\mathrm{char_i}  ]$.

\begin{figure}[!htb]
  \includegraphics[width=0.5\textwidth]{neural_model.pdf}
  \caption{Schematic overview of our neural architecture when using two encoders (\textsc{bert}{} and characters).\label{fig:model}}
\end{figure}

\inlineheader{Characters in two encoders} In the two-encoder setup, we run separate (but structurally identical) bi-LSTM encoders over the tokens and characters, and concatenate the resulting context vector before we feed it to the decoder:

\begin{equation*}
    \vect{d}_0 = \tanh \left( \matr{W}_\mathrm{init} \left[ \vect{c}_\mathrm{tok}; \vect{c}_\mathrm{char} \right] \right)
\end{equation*}

In the decoder, we replace the LSTM with a doubly-attentive LSTM, based on the doubly-attentive GRU \citep{calixto-etal-2017-doubly}. We apply soft-dual attention \citep{junczys-dowmunt-grundkiewicz-2017-exploration} to be able to attend over both encoders in the decoder (also see Figure~\ref{fig:model}):

\vspace{-0.4cm}
\begin{alignat*}{2}
    &\vect{d}_j^{\prime} &&= \text{LSTM}_1 \left( \vect{d}_{j-1}, \vect{e}_{t_{j-1}}  \right) \\
    &\vect{a}_j &&= \left[ \text{ATT}\left( \matr{C}_\mathrm{tok},\vect{d}_j^{\prime}   \right); \text{ATT}\left( \matr{C}_\mathrm{char},\vect{d}_j^{\prime}   \right) \right] \\
    &\vect{d}_j &&= \text{LSTM}_2 \left( \vect{d}^{\prime}_j, \vect{a}_j   \right)
\end{alignat*}
\vspace{-0.4cm}

Here, $\vect{e}_{t_{j-1}}$ is the embedding of the previously decoded symbol $t$,  $C$ the set of encoder hidden states for either the tokens or characters, ATT the attention function (dot-product) and $\vect{d}_j$ the final decoder hidden state at step $j$. This model can easily be extended to more than two encoders, which we will experiment with in Section~\ref{sec:results}.

This type of multi-source model is commonly used to represent different languages, e.g., in machine translation \citep{zoph:16,firat-etal-2016-multi} and semantic parsing \citep{susanto2017neural, duong2017multilingual}, though it has also been successfully applied in multi-modal translation \citep{libovicky-helcl-2017-attention}, multi-framework semantic parsing \citep{stanovsky-dagan-2018-semantics} and adding linguistic information \citep{currey:18, van-noord-etal-2019-linguistic}. To the best of our knowledge, we are the first to represent the characters as a source of extra information in a multi-source sequence-to-sequence model.

\inlineheader{Transformer} We also experiment with the Transformer model \citep{transformer:17}, using the stacked self attention model as implemented in AllenNLP. A possible advantage of this model is that it might handle longer sentences and documents better. However, it might be harder to tune \citep{popel2018training}\footnote{Also see: \url{https://twitter.com/srush_nlp/status/1245825437240102913}} and its improved performance has mainly been shown for large data sets, as opposed to the generally smaller semantic parsing data sets (Section~\ref{sec:data}). Indeed, we cannot outperform the LSTM architecture (see Section~\ref{sec:results}), even when tuning more extensively. We therefore do not experiment with adding character-level representations to this architecture, though the char-CNN can be added similarly as for the LSTM model.

\inlineheader{Hyper-parameters} To make a fair comparison, we conduct an independent hyper-parameter search on the development set for all nine input text representations (see Section~\ref{sec:lms}) across the two neural architectures, starting from the settings of \citet{van-noord-etal-2019-linguistic}. We found that the best settings were very close for all systems, with the only notable difference that the learning rate of the Transformer models is considerably smaller than for the bi-LSTM models ($0.0002$ vs $0.001$).\footnote{See Appendix~\ref{sec:hyperappendix} for specific hyperparameter settings.}

For the char-CNN model, we use $100$ filters, an embedding size of $75$ and n-gram filter sizes of $[1, 2, 3]$ for English and $[1, 2, 3, 4, 5]$ for German, Italian and Dutch. For experiments where we add characters or linguistic features, the only extra search we do is the size of the hidden vector of the RNN encoder ($300 - 600$), since this vector now has to contain more information, and could potentially benefit from a larger size. Note that (possible) improved performance is not simply due to larger model capacity, since during tuning of the baseline models a larger RNN hidden size did not result in better performance.

\subsection{Representations}
\label{sec:lms}

We will experiment with five well-known pretrained language models: \textsc{elmo}{} \citep{Peters:2018}, \textsc{bert}{} base/large \citep{bert:19} and \textsc{roberta}{} base/large \citep{roberta_arxiv:19}.\footnote{We are aware that there exist several other large pre-trained language models (e.g., \citealp{yang2019xlnet,2019t5,clark2020electra}), but we believe that the models we used have had the largest impact on the field.} The performance of these five large LMs is contrasted with results of a character-level model and three word-based models. The word-based models either learn the embeddings from scratch or use non-contextual \textsc{glove}{} \citep{glove:14} or \textsc{fasttext}{} \citep{grave2018learning} embeddings.
Pre- and postprocessing of the DRSs is done using the method described in \citet{drstacl:18}.\footnote{\url{https://github.com/RikVN/Neural_DRS/}} The DRSs are linearized, after which the variables are rewritten to a relative representation. The character-level model has character representations for the DRS concepts and constants, but not for variables, roles and operators. For all word-level models, the DRS concepts are initialized with \textsc{glove}{} embeddings, while the other target tokens are learned from scratch.

\inlineheader{BERT specifics} For the \textsc{bert}{} models, we obtained the best performance by only keeping the vector of the first WordPiece per original token (e.g., only keep \texttt{play} out of \texttt{play \#\#ing}). For \textsc{roberta}{}, it was best to use the WordPiece tokenization as is. Since linguistic features are added on token level, we duplicate the semantic tags for multi-piece tokens of \textsc{roberta}{} in Table~\ref{tab:lmcomparison}. Interestingly, we found that for both \textsc{bert}{} and \textsc{roberta}{}, it was best to keep the pretrained weights frozen. This was not a small difference: models using fine-tuning always obtained low scores ($45$ to $60$).

\subsection{Data and Evaluation}
\label{sec:data}

We use PMB releases 2.2.0 and 3.0.0\footnote{\url{https://pmb.let.rug.nl/data.php}} in our experiments (Table~\ref{tab:releases}). The latter is a larger and more diverse extension of 2.2.0, which will be used for most of our experiments. We use 2.2.0 to compare to previous work and to verify that our results are robust across datasets. The PMB releases contain DRSs for four languages (English, German, Italian and Dutch) for three levels of annotation: gold (fully manually checked), silver (partially manually corrected) and bronze (no manual corrections). To make a fair comparison to previous work, we only employ the gold and silver data, by pretraining on gold $+$ silver data and subsequently fine-tuning on only the gold data. If there is no gold train data available, we train on silver $+$ bronze and fine-tune on silver. Unless otherwise indicated, our results are on the English development set of release 3.0.0.

\begin{table}[!t]
\centering
\setlength{\tabcolsep}{4pt}
\resizebox{0.95\columnwidth}{!}{
\begin{tabular}{llrrrrr}
\toprule
& & \multicolumn{3}{c}{\textbf{Gold}} & \multicolumn{1}{c}{\textbf{Silver}} & \multicolumn{1}{c}{\textbf{Bronze}} \\
& & \multicolumn{1}{c}{Train} & \multicolumn{1}{c}{Dev} & \multicolumn{1}{c}{Test} & \multicolumn{1}{c}{Train} & \multicolumn{1}{c}{Train} \\
 \midrule
\bf 2.2.0 & English & 4,597 & 682 & 650 & 67,965 & 120,662 \\
& German & 0 & 727 & 747 & 4,235 & 102,998 \\
& Italian & 0 & 374 & 400 & 2,515 & 61,504 \\
& Dutch & 0 & 370 & 341 & 1,051 & 20,554 \\
\midrule
\bf 3.0.0 & English & 6,620 & 885 & 898 & 97,598 & 146,371\\
& German & 1,159 & 417 & 403 & 5,250 & 121,111 \\
& Italian & 0 & 515 & 547 & 2,772 & 64,305 \\
& Dutch & 0 & 529 & 483 & 1,301 & 21,550\\
\bottomrule
\end{tabular}
}
\caption{Number of documents for the four languages, for the two PMB releases considered.\label{tab:releases}}
\end{table}

\inlineheader{Linguistic features} We want to contrast our method of character-level information to adding sources of linguistic information. Based on \citet{van-noord-etal-2019-linguistic}, we employ these five sources: part-of-speech tags (POS), dependency parses (DEP), lemmas (LEM), CCG supertags (CCG) and semantic tags (SEM). For the first three sources, we use Stanford CoreNLP \citep{manning2014stanford} to parse the documents in our dataset. The CCG supertags are obtained by using easyCCG \citep{lewisSteedman:14}. For semantic tagging, we train our own trigram-based tagger using TnT \citep{Brants:2000}.\footnote{This tagger is also used in the PMB pipeline, see \citet{semantic-tagset:17}. It outperformed an ngram-based CRF-tagger \citep{crf:01} we also tried, obtaining an accuracy of $94.4\%$ on the dev set.} Table~\ref{tab:ling} shows a tagged example sentence for all five sources of information. Moreover, we also include non-contextual \textsc{glove}{} and \textsc{fasttext}{} embeddings as an extra source of information.

We add these sources of linguistic information in the same way as we add the character-level information, in either one or two encoders (see Section ~\ref{sec:neural}). In two encoders, we can use the exact same architecture. For one encoder, we (obviously) do not use the char-CNN, but learn a separate embedding for the tags (of size 200), that is then concatenated to the token-level representation, i.e., $\vect{e}_i = [ \vect{e}_\mathrm{tok_i}; \vect{e}_\mathrm{ling_i}  ]$. If we use two encoders with a LM, characters \emph{and} linguistic information (e.g., Table~\ref{tab:charplusling}), the characters are added separately in the second encoder, while the LM and linguistic information representations are added in the first encoder.

\begin{table}[!tb]
\centering
\renewcommand{\arraystretch}{1.2}
\setlength{\tabcolsep}{2pt}
\resizebox{\columnwidth}{!}{

\begin{tabu}{lllllllll}
\toprule
\textbf{Sent} & I & have & n't & been & to & Boston & since & 2013 \\
\midrule
\textbf{POS} & \postag{PRP} & \postag{VBP} & \postag{RB} & \postag{VBN} & \postag{TO} & \postag{NNP} & \postag{IN} & \postag{CD} \\
\textbf{SEM} & \semtag{PRO} & \semtag{NOW} & \semtag{NOT} & \semtag{EXT} & \semtag{REL} & \semtag{GPE} & \semtag{REL} & \semtag{YOC} \\
\textbf{LEM} & \lemtag{I} & \lemtag{have}  & \lemtag{not}  & \lemtag{be}  & \lemtag{to}  & \lemtag{Boston}  & \lemtag{since}  & \lemtag{2013} \\
\textbf{DEP} & \deptag{nsubj} & \deptag{aux} & \deptag{neg} & \deptag{cop} & \deptag{case} & \deptag{ROOT} & \deptag{case} & \deptag{nmod} \\
\textbf{CCG}  & \ccgtag{NP}    & \ccgtag{VP\textbackslash{}VP} & \ccgtag{VPVP} & \ccgtag{VP/PP} & \ccgtag{PP/NP} & \ccgtag{N} & \ccgtag{\small(VP\textbackslash{}VP)/NP} & \ccgtag{N}   \\
\bottomrule
\end{tabu}
}
\caption{\label{tab:ling}Example representation for each source of linguistic information (PMB document p00/d1489).}
\vspace*{-0.3cm}
\end{table}

\inlineheader{Evaluation} We compare the produced DRSs to the gold standard using Counter \citep{pmb-LREC:18}, which calculates micro precision, recall and F1-score based on the number of matching clauses.\footnote{\url{https://github.com/RikVN/DRS_parsing/}} We use Referee \citep{drstacl:18} to ensure that the produced DRSs are syntactically and semantically well-formed (i.e., no free variables, no loops in subordinate relations) and form a connected graph. DRSs that are ill-formed get an F1-score of $0.0$. All shown scores are averaged F1-scores over five training runs of the system, in which the same five random seeds are used.\footnote{Standard deviations are omitted for brevity, though available for all experiments here: \url{https://github.com/RikVN/Neural_DRS/}} For significance testing we use approximate randomization \citep{random-appr:89}, with $\alpha = 0.05$ and $R = 1000$.

We also introduce and release \textbf{\textsc{drs-jury}}{}. This program provides a detailed overview of the performance of a DRS parser, but can also compare experiments, possibly over multiple runs. Features include significance testing, semantic tag analysis (Section~\ref{sec:semanalysis}), sentence length plotting (Section~\ref{sec:lenanalysis}), new detailed Counter scores (Appendix~\ref{app:detailed}), and analysing (relative) best/worst produced DRSs (Appendix~\ref{app:sents}). We hope this is a step in the direction of a more principled way of evaluating DRS parsers.

\vspace{-0.1cm}
\section{Results}
\label{sec:results}
\vspace{-0.1cm}

\inlineheader{LMs vs char-level models} DRS parsing is no exception to the general trend in NLP: it is indeed the case that the pretrained language models outperform the char-only model (Table~\ref{tab:charvsbert}). Interestingly, the Transformer model has worse performance for all representations.\footnote{The Transformer models were even tuned longer, since they were more sensitive to small hyperparameter changes.} Surprisingly, we find that \textsc{bert-base}{} is the best model, though the differences are small.\footnote{\textsc{bert-base}{} significantly outperformed all the other models, except for \textsc{bert-large}.} We use this model in further experiments (referred to as \textsc{bert}{}).

\inlineheader{Adding characters to BERT} We can see the impact of adding characters to \textsc{bert}{} (first row of results in Table~\ref{tab:charplusling}). For both methods, it results in a clear and significant improvement over the \textsc{bert}{}-only baseline, 87.6 versus 88.1.

\begin{table}[!tb]
\centering
\setlength{\tabcolsep}{3pt}
\resizebox{0.875\columnwidth}{!}{
\begin{tabular}{lcc}
\toprule
                             & \textbf{bi-LSTM} & \textbf{Transformer} \\
 \midrule
\bf Char & 86.1 & 79.7 \\
\bf Word & 85.3 & 83.6\\
\bf  \textsc{glove}{} & 85.4 & 84.6 \\
\bf \textsc{fasttext}{} & 85.5 & 84.0  \\
\bf  \textsc{elmo}{} & 87.3  & 84.3   \\
\bf  \textsc{bert-base}{} & \bf 87.6 & \bf 85.4 \\
\bf  \textsc{bert-large}{} & 87.5  & 84.7     \\
\bf  \textsc{roberta-base}{} & 87.0  & 82.7    \\
\bf  \textsc{roberta-large}{} & 86.8  & 81.9  \\
\bottomrule
\end{tabular}
}
\caption{\label{tab:charvsbert}Baseline model for the nine input representations considered, for the bi-LSTM and Transformer architectures. Best score in each column shown in bold.}
\end{table}

\begin{table}[!tb]
\centering
\setlength{\tabcolsep}{4pt}
\resizebox{\columnwidth}{!}{
\begin{tabular}{l|cc|ccc}
\toprule
  & \multicolumn{2}{c|}{\textbf{No chars}} & \multicolumn{3}{c}{\textbf{+ characters}} \\
  & \textbf{1-enc} & \textbf{2-enc} & \textbf{1-enc} & \textbf{2-enc} & \textbf{3-enc} \\
\midrule
\textbf{\textsc{bert}}            & 87.6   &  NA      &  \bf 88.1   &   \bf 88.1    & NA        \\
\midrule
\textbf{\textsc{bert}{} + word} & 87.7 & 87.4 & 87.8 & 87.6 & 86.9\\
\textbf{\textsc{bert}{} + \textsc{glove}{}}    & 87.9   & 87.2    &  88.1       & 88.0 & 86.9  \\
\textbf{\textsc{bert}{} + \textsc{fasttext}{}} & 87.8   & 87.7    &  87.9      & 87.9  & 87.0   \\
\textbf{\textsc{bert}{} + pos}      & 87.6   & 87.6       &  87.4      & 87.6  & 87.8 \\
\textbf{\textsc{bert}{} + sem}      & 87.9   & 88.0       &  88.0      & \bf 88.4 & 88.1  \\
\textbf{\textsc{bert}{} + lem}      & 87.8   & 88.0       &  88.1      & 88.0  & 87.4 \\
\textbf{\textsc{bert}{} + dep}      & 87.9   & 87.5       &  88.0       & 87.8  & 87.8  \\
\textbf{\textsc{bert}{} + ccg}      & 87.8   & 87.3       & 87.9     & 87.8  & 87.6  \\
\bottomrule
\end{tabular}
}
\caption{Results for adding characters, linguistic information and a combination of the two to the bi-LSTM \textsc{bert-base}{} model on 3.0.0 English dev.\label{tab:charplusling}}
\vspace{-0.3cm}
\end{table}

\inlineheader{Adding linguistic features to BERT} However, another common method of improving performance is adding linguistic features to the token-level representations. We try a range of linguistic features (described in Section~\ref{sec:data}), that are added in either one or two encoders. We see in the first two columns of results of Table~\ref{tab:charplusling} that even though linguistic information sources indeed do improve performance (up to $0.4$ absolute), there is no single source that can beat adding just the character-level representations ($88.1$).

\inlineheader{Combining characters and linguistic features} An obvious follow-up question is whether we still see improvements for character-level models when also adding linguistic information.
In a single encoder, adding characters (third column of results in Table~\ref{tab:charplusling}) is beneficial for 6 out of 7 linguistic sources (i.e., compared to the first column of results). The scores are, however, not higher than simply adding characters on their own, suggesting that linguistic features are not always beneficial if character-level features are also included. For two encoders, the pattern is less clear, but we do find our highest score thus far when we combine characters and semantic tags (88.4).\footnote{This improvement is significant. With gold semantic tags (ceiling performance) we score $88.6$.} Using three encoders did not yield clear improvements over two encoders. Therefore, we do not experiment with using more than three encoders.

\begin{table}[!tb]
\centering
\setlength{\tabcolsep}{2pt}
\resizebox{\columnwidth}{!}{
\begin{tabular}{lcccc}
\toprule
                       & \textbf{No char} & \textbf{\begin{tabular}[c]{@{}l@{}}+char\\ (1 enc)\end{tabular}} & \textbf{\begin{tabular}[c]{@{}l@{}}+char\\ (2 enc)\end{tabular}} & \textbf{\begin{tabular}[c]{@{}c@{}}+char +sem\\       (2 enc)\end{tabular}} \\
\midrule
\textbf{\textsc{elmo}}         & 87.3   & 87.6    & 87.8    & \bf 88.0      \\
\textbf{\textsc{bert-base}}     & 87.6   & 88.1    & 88.1    & \bf 88.4        \\
\textbf{\textsc{bert-large}}    & 87.5   & 88.2    & 87.8    & \bf 88.3       \\
\textbf{\textsc{roberta-base}}  & 87.0   & 87.3    & 87.8    & \bf 88.0       \\
\textbf{\textsc{roberta-large}} & 86.8   & 86.8    & 87.0    & \bf 87.3       \\
\bottomrule
\end{tabular}
}
\caption{Results on 3.0.0 English dev of four LMs for adding characters and both characters and semtags.\label{tab:lmcomparison}}
\vspace{-0.3cm}
\end{table}

\begin{figure*}[!htb]
\centering
\begin{subfigure}{.5\textwidth}
  \centering
  \includegraphics[width=\columnwidth]{barplot_2.2.0_new.pdf}
  \caption{Scores for PMB release 2.2.0.}
\end{subfigure}%
\begin{subfigure}{.5\textwidth}
  \centering
 \includegraphics[width=\columnwidth]{barplot_3.0.0_withchar.pdf}
   \caption{Scores for PMB release 3.0.0.}
  \label{fig:sub2}
\end{subfigure}
\caption{\label{fig:nonen}Dev and test scores (F1) for the four models we trained for three languages (German, Italian and Dutch). For 2.2.0, we compare our results to \citet{fancellu-etal-2019-semantic}.}
\vspace{-0.3cm}
\end{figure*}

\inlineheader{Robustness to different LMs} We want to verify that the character improvements are robust to using different language models (Table~\ref{tab:lmcomparison}). We see that adding characters results in improvement for all the LMs under consideration, even for \textsc{elmo}{}, which already incorporates characters in creating the initial embeddings. Moreover, combining characters and semantic tags also results in an improvement over just using characters for all the LMs considered.

\inlineheader{Robustness across languages} We train systems for German, Italian and Dutch for four models: char-only, \textsc{bert-only}{}, \textsc{bert}{} + char in 1 encoder, and \textsc{bert}{} + char in two encoders.\footnote{We do not train a model that uses semantic tags as features, since there is not enough gold semantic tag data available to train a good tagger for any of these languages.} The \textsc{bert}{} model we use is \texttt{bert-multilingual-uncased}. The results for both PMB releases are shown in Figure~\ref{fig:nonen}. For all languages, adding characters leads to a clear improvement for both one and two encoders, though for Dutch the improvement is smaller than for German and Italian. Interestingly, the two-encoder setup seems to be preferable for these smaller, non-English data sets. For 2.2.0, we outperform the system of \citet{fancellu-etal-2019-semantic} for German and Italian and obtain competitive scores for Dutch.

\begin{table}[!htb]
\setlength{\tabcolsep}{4pt}
\resizebox{\columnwidth}{!}{
\begin{tabular}{l|rr|rr}
\toprule
  & \multicolumn{2}{c|}{\textbf{2.2.0}} & \multicolumn{2}{c}{\textbf{3.0.0}} \\
  & \textbf{Dev} & \textbf{Test} & \textbf{Dev} & \textbf{Test} \\
\midrule
\textbf{Amateur Boxer}  & 72.2 & 72.2 & 78.2  & 78.8 \\
\textbf{Pro Boxer}  & NA & NA & 88.2  & 88.9 \\
\textbf{\citet{fancellu-etal-2019-semantic}} & NA & 76.4 & NA & NA \\
\textbf{\citet{evang-2019-transition}}  & 74.4 & 74.4 & NA & NA \\
\textbf{\citet{drstacl:18}} & 81.2 & 83.3 & 84.3 & 84.9  \\
\textbf{\citet{van-noord-etal-2019-linguistic}} & 86.5 & 86.8 & 86.8  & 87.7  \\
\textbf{\citet{liu-etal-2019-discourse}} & 85.5 & 87.1 & NA & NA \\
\midrule
\textbf{This work - \textsc{bert}{}} & 85.4 & 87.9 & 87.6 & 88.5 \\
\textbf{This work - \textsc{bert}{} + char (1 enc)} & \bf 86.1 & \bf 88.3 & 88.1 & 89.2  \\
\textbf{This work - \textsc{bert}{} + char (2 enc)} & 85.6 & 88.1 & 88.1 & 89.0 \\
\textbf{This work - Best model} & 85.5 & 87.7 & \bf 88.4 & \bf 89.3 \\
\bottomrule
\end{tabular}
}
\caption{Comparison of our four main models to previous work for PMB 2.2.0 and 3.0.0 (English only).\label{tab:finalres}}
\vspace{-0.3cm}
\end{table}

\inlineheader{Comparison to previous work} To check whether the improvements hold on unseen data, we run our best models on the test set and compare the scores to previous work (Table~\ref{tab:finalres}).\footnote{For the detailed Counter scores see Appendix~\ref{app:detailed}.}
We see that adding the character-level information has similar (significant) improvements for dev and test on both data sets. The addition of semantic tags might be questionable: for 2.2.0, both the \textsc{bert}{} + char models outperform this model, while for 3.0.0 the 0.1 improvement over \textsc{bert}{} + char in one encoder is not significant. Despite this, we reach state-of-the-art performance on both data sets, significantly outperforming the previous best scores by \citet{van-noord-etal-2019-linguistic} and \citet{liu-etal-2019-discourse}. We also compare to the semantic parser Boxer, which needs input for 6 different PMB layers \citep{eacl:pmb}. \emph{Amateur Boxer} is trained with internal PMB taggers, while \emph{Pro Boxer} uses the output of a neural multi-task learning system based on \textsc{bert}{} \citep{machamp:20}. Even though this is an unfair comparison to our system, since the rule-based components of Boxer are (partly) optimized on the dev and test sets, our best model still improves slightly over \emph{Pro Boxer} (significantly on test).

\section{Analysis}
\label{sec:analysis}

\begin{table}[!t]
\centering
\setlength{\tabcolsep}{1pt}
\resizebox{\columnwidth}{!}{
\begin{tabular}{lrcccc}
\toprule
& \textbf{\# Docs} & \textbf{\textsc{bert}{}} & \textbf{+char} & \textbf{+char} & \textbf{+ch+sem} \\
& & & \bf (1 enc) & \bf (2 enc) & \bf (2 enc) \\
\midrule
\bf All sentences & 1,783 & 88.1 & 88.7 & 88.5 & \bf 88.8 \\
\midrule
\bf Modality            & 188 & 86.8 & \cellcolor{green} +0.1 & \cellcolor{green} +0.1 & \cellcolor{green} \bf +0.4 \\
\bf \quad Negation    & 156 & 88.8 & \cellcolor{green} +0.2 & \cellcolor{red} -0.1 & \cellcolor{green} \bf +0.4 \\
\bf \quad Possibility & 38  & 81.3 & \cellcolor{orange} 0.0 & \cellcolor{green} +1.0 & \cellcolor{green} \bf +1.5 \\
\bf \quad Necessity   & 13  & 74.5 & \cellcolor{red} -1.6 & \cellcolor{green} \bf +1.4 & \cellcolor{red} -0.2 \\
\bf Logical & 449 & 86.3 & \cellcolor{green} \bf +0.7 & \cellcolor{green} +0.2 & \cellcolor{green} +0.5 \\
\bf Pronouns & 996 & 88.9 & \cellcolor{green} +0.4 & \cellcolor{green} +0.4 & \cellcolor{green} \bf +0.6 \\
\bf Attributes & 1,063 & 87.6 & \cellcolor{green} +0.7 & \cellcolor{green} +0.4 & \cellcolor{green} \bf +0.8 \\
\bf Comparatives & 45 & 84.5 & \cellcolor{green} \bf +1.6 & \cellcolor{green} +0.2 & \cellcolor{red} -0.2 \\
\bf Named entities & 673 & 88.1 & \cellcolor{green} +0.5 & \cellcolor{green} +0.3 & \cellcolor{green} \bf +0.6 \\
\bf Numerals & 186 & 85.8 & \cellcolor{green} +1.1 & \cellcolor{green} +1.2 & \cellcolor{green} \bf +1.5 \\
\bottomrule
\end{tabular}
}
\caption{\label{tab:semtag}F-scores on subsets of sentences that contain a certain phenomenon, based on semantic tags, for the combined dev and test set of PMB release 3.0.0. Full scores shown for \textsc{bert}{} and absolute differences for the remaining systems.}
\vspace*{-0.2cm}
\end{table}

\subsection{Semantic tag analysis}
\label{sec:semanalysis}

We are also interested in finding out \emph{why} the character-level representations help improve performance. As a start, we investigate on what type of sentences and semantic phenomena the character representations are the most beneficial. We introduce a novel method of analysis: selecting subsets of sentences based on the occurrence of certain semantic tags. In the PMB release, each token is also annotated with a semantic tag, which indicates the semantic properties of the token in the given context \cite{semantic-tagset:17}. This allows us to easily select all sentences that contain certain (semantic) phenomena and evaluate the performance of the different models on those sentences.\footnote{Note that this method of analysis can easily be used for other NLP tasks as well, the only requirement being that a semantic tagger has to be used to get the semantic tags.}

The selected phenomena and corresponding F-scores for our four best models (see Table~\ref{tab:finalres}) are shown in Table~\ref{tab:semtag}.\footnote{See Appendix~\ref{sec:appsemtags} for the list of semtags per category.} Our best model (+ch+sem) has the best performance on six of the seven phenomena selected, even though the differences are small. The character-level representations seem to help across the board; the +char models improve on the baseline (\textsc{bert}) in almost all instances.

For \emph{Numerals} and \emph{Named Entities} we expected the characters to help specifically, since (i) \textsc{bert}{} representations might not be as optimal for all individual numerals \citep{wallace-etal-2019-nlp}, and (ii) the character representations might attend more to capital letters, which often indicate the presence of a named entity. Indeed, the character representations clearly help for \emph{Numerals}, but less so for \emph{Named Entities}. Of course, this analysis only scratched the surface as to why the character-level representations improve performance. We leave a more detailed investigation to future work.

\subsection{Sentence length analysis}
\label{sec:lenanalysis}

We are also interested in finding out which model performs well on longer documents.
When the Transformer model was introduced, one of the advantages was less decrease in performance for longer sentences \citep{transformer:17}. Also, since Boxer is partly rule-based and not trained in an end-to-end fashion, it might be able to handle longer sentences better. Figure~\ref{fig:senlen} shows the performance over sentence length for seven of our trained systems. We see a similar trend for all models: a decrease in performance for longer sentences.
We also create a regression model that predicts F-score, with as predictors parser and document length in tokens, similar to \citet{drstacl:18}. We do not find a significant interaction of any model with sentence length, i.e., none of the models decreases significantly less or more than any other model.

\begin{figure}[!t]
  \includegraphics[width=\columnwidth]{senlen_7parsers_new.pdf}
  \caption{F-scores over document length (tokens) on the combined English dev and test set of 3.0.0. X-axis shows document length (top) and the number of documents for that length (bottom).\label{fig:senlen}}
  \vspace*{-0.2cm}
\end{figure}

\begin{figure}[!t]
  \includegraphics[width=\columnwidth]{binned_barplot.pdf}
  \caption{F-scores over document length (tokens) on the silver standard evaluation set of longer documents. X-axis shows the sentence length bins (top) and the number of documents for that length (bottom).\label{fig:silvereval}}
  \vspace*{-0.2cm}
\end{figure}

To get some idea how well our models would do on longer (possibly multi-sentence) documents, we create a new evaluation set. We select all silver documents with 15 or more and less than 51 tokens that have at least the semtagging or CCG layer marked as gold standard. This resulted in a set of 128 DRSs, which should contain the higher quality silver documents. We retrain our models with those sentences removed and plot the performance over sentence length in Figure~\ref{fig:silvereval}. We see that performance still decreases for longer sentences, though not as much after 30 tokens per document. The Transformer model does not seem to catch up with the bi-LSTM models, even for longer documents. The addition of characters is still beneficial for longer documents, though only in one encoder.

\subsection{Discussion}

We found that adding character-level representations generally improved performance, though we did not find a clear preference for either the one-encoder or two-encoder model.
We believe that, given the better performance of the two-encoder model on the fairly short documents of the non-English languages (see Figure~\ref{fig:nonen}), this model is likely the most useful in semantic parsing tasks with single sentences, such as SQL parsing \citep{zelle1996learning, iyer-etal-2017-learning, finegan-dollak-etal-2018-improving}, while the one encoder char-CNN model has more potential for tasks with longer sentences/documents, such as AMR \citep{amr:13}, UCCA \citep{ucca:13} and GMB-based DRS parsing \citep{GMB:2017,neural_drs_gmb:18, liu-etal-2019-acl}. The latter model also has more potential to be applicable for other (semantic parsing) systems as it can be applied to all systems that form token-level representations from a document. In this sense, we hope that our findings here are also applicable for other, more structured, encoder-decoder models developed for semantic parsing (e.g., \citealp{yin-neubig-2017-syntactic,krishnamurthy-etal-2017-neural,coarsefine:18,liu-etal-2019-acl}).

An unexpected finding is that the \textsc{bert}{} models outperformed the larger \textsc{roberta}{} models. In addition, it was even preferable to use \textsc{bert}{} only as initial token embedder, instead of fine-tuning using the full model.
Perhaps this is an indication that certain NLP tasks cannot be solved by simply training ever larger language models.
Moreover, the Transformer model did not improve performance for any of the input representations, while being harder to tune as well. We are a bit hesitant with drawing strong conclusions here, though, since we only experimented with a vanilla Transformer, while recent extensions (e.g., \citealp{universaltransformer:19,guo-etal-2019-star,press-etal-2020-improving}) might be more promising for smaller data sets.

\section{Conclusion}

We performed a range of experiments on Discourse Representation Structure Parsing using neural sequence-to-sequence models, in which we vary the neural representation of the input documents. We show that, not surprisingly, using pretrained contextual language models is better than simply using characters as input (\textbf{RQ1}). However, characters can
still be used to improve performance, in both a single encoder and two encoders (\textbf{RQ2}). The improvements are larger than using individual sources of linguistic information, and performance still improves in combination with these sources (\textbf{RQ3}). The improvements are also robust across different languages models, languages and data sets (\textbf{RQ4}) and improve performance across a range of semantic phenomena (\textbf{RQ5}). These methods should be applicable to other semantic parsing and perhaps other natural language analysis tasks.

\section*{Acknowledgements}

This work was funded by the NWO-VICI grant ``Lost in Translation---Found in Meaning'' (288-89-003).
The Tesla K40 GPU used in this work was kindly donated to us by the NVIDIA Corporation.
We thank the Center for Information Technology of the University of Groningen for providing access to the Peregrine high performance computing cluster.
We also want to thank three anonymous reviewers and our colleagues Lasha Abzianidze, Gosse Minnema, Malvina Nissim, and Chunliu Wang for their comments on earlier versions of this paper.

redpajama_set_name: RedPajamaArXiv


text:
Q: multirow with word wrapped columns in longtable I am trying to create a table that looks like this using longtable. The table will have many rows so that it spans several pages.
What I'm looking for is how to create this table so that the text in the first column ('Header 1') is left-center justified in the cell and the text the second column ('Header 2') is upper left justified.
I am able to make it work by manually setting the rowcount in multirow, but since the text will change row by row, manually setting the rowcount is not efficient.  I need a generic solution that will reflow appropriately.
Sorry for the poor graphic, but due to site rules, I can't post a screenshot.

--------------------------------------------------------------
|header 1      |header 2       |header 3       |header 4     |
---------------|---------------|---------------|-------------|
|              |This is where  |               |             |
|              |some text will |               |             |
|              |go that spans  |               |             |
|This is where |several rows.  |               |             |
|a lot of text |---------------|---------------|             |
|will go that  |This is where  |               |             |
|will span     |some text will |               |             |
|several       |go that spans  |               |             |
|columns and   |several rows.  |               |             |
|needs to      |---------------|---------------|             |
|left-centered |This is where  |               |             |
|justified.    |some text will |               |             |
|              |go that spans  |               |             |
|              |several rows.  |               |             |
|--------------|---------------|---------------|-------------|

A: You say that you don't want to manually give the number of rows, but there must be some algorithm that says which rows it is to cover. If you could specify the last row, LaTeX could be persuaded to count rows. If you mean it's the whole table, then longtable is not the right package, as it won't break the spanning entry, so that would block breaking of the table so you could more simply do it with a \parbox for column 1 and a normal table for the rest, something like
\parbox[t]{....}%
\begin{tabular}[t]{lll}
header2&header3&header4\\....

redpajama_set_name: RedPajamaStackExchange


Storytelling is an aspect of culture that is tied into nearly every form of contemporary media. Through books, movies, TV shows and everything in between, a vast array of stories are constantly being fed into the world. However, the power of stories is not limited to merely these mediums. In a studio in Juneau, Alaska, a different method of storytelling is taking place — one that has been passed down for generations through immensely talented indigenous artists. Read more.
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text:
Stephan Andrade has been planning his revenge ever since Dominic Corisi unscrupulously took over his father's company. With Corisi Enterprises gambling its reputation on the success of a new software network for China, Stephan finally has his chance to take back his legacy. Dominic's younger sister, Nicole, asks Stephan for his help and provides him with an opportunity to exact his revenge on a personal level. It all goes smoothly until he falls in love.
Stolen Kisses
Dax Marshall isn't the type of man a woman takes home to meet her parents. He's a business shark who has never let anything as insignificant as caring stop him from taking over a company. Some call him heartless, but he doesn't see the value of caring about anyone or anything until he meets Kenzi. Her happiness becomes his obsession. Kenzi Barrington has tried to be the person her family needed her to be, but she doesn't want to lie anymore. She turns to the one man she knows is strong enough to hear the truth.
Narrated by: Teddy Hamilton, Lidia Dornet
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By Margaret on 21-10-2020
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If she'll stop fighting him long enough, he'll show her just how perfect they are for each other. That is, if she doesn't get them both killed first.
Book 1: Maid for the Billionaire
Book 2: For Love or Legacy
Book 3: Bedding the Billionaire
Book 4: Saving the Sheikh
Book 5: Rise of the Billionaire
Book 6: Breaching the Billionaire: Alethea's Redemption
What listeners say about Breaching the Billionaire
Loved it a great storyline. The whole series was great. Look forward to the next series.
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I didn't like Alethia much in the other books. but knowing her story and why she does what she does was really nice.
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Who Has Alethea's Back?
I really didn't like Alethea in the last book, and couldn't imagine what the author could do to make her more likable. I read and listened to this book. It was entertaining, but the entertainment came mostly from the guys and their relationships with each other. The situation Alethea discovered and was trying to solve waa also interesting. I didn't care much for the narration. It didn't add anything to the book. I still didn't care much for her character because she was so angry and defensive all of the time. I did admire her security talents, and her intelligence. I was happy that Marc had her back. It was sad that she felt so awkward and ostracized from everyone. It was nice when all of that was resolved. A book with romance, intrigue, mystery, and some heat.
Isha Coleman
...a sweetheart of a story.
Alethea has always been one of my favorite Ruth Cardello characters. Not because she's perfect. In fact she's anything but perfect. Her appeal is that she is not afraid to embrace those flaws even at her most vulnerable. Marc is the total opposite. Vulnerability is not something he strives for. Yet his heart has other plans. Breaching the Billionaire can be as prickly as it wants because underneath that bitterness lies a sweetheart of a story.
cassity
really good moments that made me laugh
good have to read it with the whole series some part made me laugh really hard.
Storeyline
EXCELLENT! I loved this book. It has absolutely everything I love in a EXCELLENT storey and held my attention until the very end. On to the next Ruth Cardello!
You can find your match
I was happy Althea found her Romeo. It was hard that the story included more individual dialogue for previous characters then necessary, but it was good
Dawn Yacovetta
It was a fantastic story of love and liberation!
Would you consider the audio edition of Breaching the Billionaire: Alethea's Redemption to be better than the print version?
I like the printed version, because I hear the voices in my head as they should be - the men sound more male and the women sound more mature. However, I must say that Kim Bubbs has a way with accents and is spectacular as switching from one character to another in an instant. She has a great memory for the sound and accent she's given to each of them.
What was one of the most memorable moments of Breaching the Billionaire: Alethea's Redemption?
I love the bunker scene when Marc finally took Alethea. Who wouldn't. It was spontaneous and erotic. Marc's words were full of emotion and it was as romantic as it could be with Alethea being held captive. Granted, the way the swam naked with each other, it was impossible to believe that they'd be able to hold out. But when it finally happened, it was like an explosion.
Which scene was your favorite?
My favorite scene had to be the one where she jumped into his arms after the craziness with Sliver toward the end of the story. They were both so exposed. And after the danger they'd been in, everything was so raw.
The part that moved me most was when he put so much effort in getting Alethea back together with Lil and the rest of the clan. It wasn't just important to him that she know how much he loved her, but it was important to show her the depth of that love by giving her back the one thing that was most important, her relationship with Lil.
Ruth Cardello has a style that is intense and romantic. All of her characters are broken and they way she works to get them to where they need to be, so they can find their happily-ever-after is like magic. I especially enjoyed seeing these two characters begin healing each other. Her work is on a level all its own. I'm looking forward to seeing what she has in store for us next.
Donna Young 🐶🐕🐱🐈
This is book six in Ruth Cardello's Legacy Books
This is book six in Ruth Cardello's Legacy collection The ever safety conscious Alethea is at a crossroad. For as long as Alethea could remember, it's her and her one true best friend, Lil, against the world. Now Lil has met Prince Jake "I have my own helicopter" Charming and has really reconnected with her family. Alethea is on the outside looking in and is trying to justify who she's always been. To people who have never understood Alethea. When Alethea uncovers a dominating threat to Lil's family, Alethea's determined to save them, even though they tell her not to get involved and assign their top best man to try stop her. The Mr. Cool and sexy Marc Stone has been watching Alethea. Marc has been watching Alethea ever since she waltzed through his supposedly high security system and lands right into his late night fantasies. As Marc is a decorated Marine, he's seen too much war. One things the army hadn't prepared him for this wild redhead. Aletha is Strong. Beautiful. Flawed, and over and highly protective. Hard to love, and impossible to deny. Can Marc and Alethea work together and get all the sparks heading in one direction, to help out their friends and keep them safe? If Alethea will stop fighting Marc long enough, Marc will show Alethea just how perfect they are for each other. That is, if Alethea doesn't get them both killed in the process. I love Ruth Cardello i so need more than 5*s

redpajama_set_name: RedPajamaCommonCrawl


text:
Las elecciones municipales de Portugal de 2013 tuvieron lugar el día 29 de septiembre de 2013.

Expectativas
Ante las políticas de austeridad llevadas a cabo por el gobierno del partido socialdemócrata, mandados por la Unión Europea, se esperaba que recibiesen una pérdida del respaldo de los ciudadanos de Portugal.

Resultados
Los 308 ayuntamientos fueron repartidos políticamente de la siguiente forma:

 El PSD salió elegido en 107 ayuntamientos.
 El Partido Socialista salió elegido en 149 ayuntamientos.
 En 6 ayuntamientos salieron elegidos candidatos independientes.
 Los 46 ayuntamientos restantes se repartieron entre distintos partidos políticos.

Referencias

2013
Portugal en 2013
Portugal

redpajama_set_name: RedPajamaWikipedia


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