ymoslem/acl-abstracts huggingface dataset:
Unfinished Business: Construction and Maintenance of a Semantically Tagged Historical Parliamentary Corpus, UK Hansard from 1803 to the present day (2020)

Creating, curating and maintaining modern political corpora is becoming an ever more involved task. As interest from various social bodies and the general public in political discourse grows so too does the need to enrich such datasets with metadata and linguistic annotations. Beyond this, such corpora must be easy to browse and search for linguists, social scientists, digital humanists and the general public. We present our efforts to compile a linguistically annotated and semantically tagged version of the Hansard corpus from 1803 right up to the present day. This involves combining multiple sources of documents and transcripts. We describe our toolchain for tagging; using several existing tools that provide tokenisation, part-of-speech tagging and semantic annotations. We also provide an overview of our bespoke web-based search interface built on LexiDB. In conclusion, we examine the completed corpus by looking at four case studies including semantic categories made available by our toolchain.
The siParl corpus of Slovene parliamentary proceedings (2020)

The paper describes the process of acquisition, up-translation, encoding, annotation, and distribution of siParl, a collection of the parliamentary debates from the Assembly of the Republic of Slovenia from 1990{--}2018, covering the period from just before Slovenia became an independent country in 1991, and almost up to the present. The entire corpus, comprising over 8 thousand sessions, 1 million speeches and 200 million words was uniformly encoded in accordance with the TEI-based Parla-CLARIN schema for encoding corpora of parliamentary debates, and contains extensive meta-data about the speakers, a typology of sessions etc. and structural and editorial annotations. The corpus was also part-of-speech tagged and lemmatised using state-of-the-art tools. The corpus is maintained on GitHub with its major versions archived in the CLARIN.SI repository and is available for linguistic analysis in the scope of the on-line CLARIN.SI concordancers, thus offering an invaluable resource for scholars studying Slovenian political history.
Who mentions whom? Recognizing political actors in proceedings (2020)

We show that it is straightforward to train a state of the art named entity tagger (spaCy) to recognize political actors in Dutch parliamentary proceedings with high accuracy. The tagger was trained on 3.4K manually labeled examples, which were created in a modest 2.5 days work. This resource is made available on github. Besides proper nouns of persons and political parties, the tagger can recognize quite complex definite descriptions referring to cabinet ministers, ministries, and parliamentary committees. We also provide a demo search engine which employs the tagged entities in its SERP and result summaries.
Challenges of Applying Automatic Speech Recognition for Transcribing EU Parliament Committee Meetings: A Pilot Study (2020)

Challenges of Applying Automatic Speech Recognition for Transcribing EUParliament Committee Meetings: A Pilot StudyHugo de Vos and Suzan VerberneInstitute of Public Administration and Leiden Institute of Advanced Computer Science, Leiden Universityh.p.de.vos@fgga.leidenuniv.nl, s.verberne@liacs.leidenuniv.nlAbstractWe tested the feasibility of automatically transcribing committee meetings of the European Union parliament with the use of AutomaticSpeech Recognition techniques. These committee meetings contain more valuable information for political science scholars than theplenary meetings since these meetings showcase actual debates opposed to the more formal plenary meetings. However, since there areno transcriptions of those meetings, they are a lot less accessible for research than the plenary meetings, of which multiple corpora exist. We explored a freely available ASR application and analysed the output in order to identify the weaknesses of an out-of-the box system. We followed up on those weaknesses by proposing directions for optimizing the ASR for our goals. We found that, despite showcasingacceptable results in terms of Word Error Rate, the model did not yet suffice for the purpose of generating a data set for use in PoliticalScience. The application was unable to successfully recognize domain specific terms and names. To overcome this issue, future researchwill be directed at using domain specific language models in combination with off-the-shelf acoustic models.
Parsing Icelandic Al\thingi Transcripts: Parliamentary Speeches as a Genre (2020)

We introduce a corpus of transcripts from Al{\th}ingi, the Icelandic parliament. The corpus is syntactically parsed for phrase structure according to the annotation scheme of the Icelandic Parsed Historical Corpus (IcePaHC). This addition to IcePaHC makes it more diverse with respect to text types and we argue that having a syntactically parsed corpus facilitates research on differt types of texts. We furthermore argue that the speech corpus can be treated somewhat like spoken language even though the transcripts differ in various ways from daily spoken language. We also compare this text type to other types and argue that this genre can shed light on their properties. Finally, we exhibit how this addition to IcePaHC has helped us in identifying and solving issues with our parsing scheme.
Identifying Parties in Manifestos and Parliament Speeches (2020)

This paper addresses differences in the word use of two left-winged and two right-winged Danish parties, and how these differences reflecting some of the basic stances of the parties can be used to automatically identify the party of politicians from their speeches. In the first study, the most frequent and characteristic lemmas in the manifestos of the political parties are analysed. The analysis shows that the most frequently occurring lemmas in the manifestos reflect either the ideology or the position of the parties towards specific subjects, confirming for Danish preceding studies of English and German manifestos. Successively, we scaled our analysis applying machine learning on different language models built on the transcribed speeches by members of the same parties in the Parliament (Hansards) in order to determine to what extent it is possible to predict the party of the politicians from the speeches. The speeches used are a subset of the Danish Parliament corpus 2009{--}2017. The best models resulted in a weighted F1-score of 0.57. These results are significantly better than the results obtained by the majority classifier (F1-score = 0.11) and by chance results (0.25) and show that building language models over the speeches used by politicians can be used to identify the politicians' party even if they debate about the same subjects and thus often use the same terminology in many cases. In the future, we will include the subject of the speeches in the prediction experiments
Comparing Lexical Usage in Political Discourse across Diachronic Corpora (2020)

Most diachronic studies on both lexico-semantic change and political language usage are based on individual or comparable corpora. In this paper, we explore ways of studying the stability (and changeability) of lexical usage in political discourse across two corpora which are substantially different in structure and size. We present a case study focusing on lexical items associated with political parties in two diachronic corpora of Austrian German, namely a diachronic media corpus (AMC) and a corpus of parliamentary records (ParlAT), and measure the cross-temporal stability of lexical usage over a period of 20 years. We conduct three sets of comparative analyses investigating a) the stability of sets of lexical items associated with the three major political parties over time, b) lexical similarity between parties, and c) the similarity between the lexical choices in parliamentary speeches by members of the parties vis-{\textquoteleft}a-vis the media`s reporting on the parties. We employ time series modeling using generalized additive models (GAMs) to compare the lexical similarities and differences between parties within and across corpora. The results show that changes observed in these measures can be meaningfully related to political events during that time.
The Europeanization of Parliamentary Debates on Migration in Austria, France, Germany, and the Netherlands (2020)

Corpora of plenary debates in national parliaments are available for many European states. For comparative research on political discourse, a persisting problem is that the periods covered by corpora differ and that a lack of standardization of data formats inhibits the integration of corpora into a single analytical framework. The solution we pursue is a {\textquoteleft}Framework for Parsing Plenary Protocols' (frappp), which has been used to prepare corpora of the Assembl{\'e}e Nationale ({\textquoteleft}{\textquoteleft}ParisParl{\textquotedblright}), the German Bundestag ({\textquoteleft}{\textquoteleft}GermaParl{\textquotedblright}), the Tweede Kamer of the Netherlands ({\textquoteleft}{\textquoteleft}TweedeTwee{\textquotedblright}), and the Austrian Nationalrat ({\textquoteleft}{\textquoteleft}AustroParl{\textquotedblright}) for the first two decades of the 21st century (2000-2019). To demonstrate the usefulness of the data gained, we investigate the Europeanization of migration debates in these Western European countries of immigration, i.e. references to a European dimension of policy-making in speeches on migration and integration. Based on a segmentation of the corpora into speeches, the method we use is topic modeling, and the analysis of joint occurrences of topics indicating migration and European affairs, respectively. A major finding is that after 2015, we see an increasing Europeanization of migration debates in the small EU member states in our sample (Austria and the Netherlands), and a regression of respective Europeanization in France and {--} more notably {--} in Germany.
Querying a large annotated corpus of parliamentary debates (2020)

The TAPS corpus makes it possible to share a large volume of French parliamentary data. The TEI-compliant approach behind its design choices facilitates the publishing and the interoperability of data, but also the implementation of exploratory data analysis techniques in order to process institutional or political discourse. We demonstrate its application to the debates occurred in the context of a specific legislative process, which generated a strong opposition.
\textquoteleftPractical', if that`s the word (2020)

Certain conditionals have something other than a clause as their consequent: their antecedent if-clauses are {\textquoteleft}adverbial clauses' without a verb. We argue that they function in a way already seen for those with clausal consequents, despite lacking the content we might expect for the formation of a conditional. The use of the if-clause with sub-clausal consequents is feasible thanks to the fact that this function does not depend on the consequent content, and so is not impeded when the consequent does not provide a proposition, question or imperative. To support this we provide meaning rules for conditionals in terms of information state updates, letting the same construction play out in different ways depending on context and content.
Personae under uncertainty: The case of topoi (2020)

In this paper, we propose a probabilistic model of social signalling which adopts a persona-based account of social meaning. We use this model to develop a socio-semantic theory of conventionalised reasoning patterns, known as topoi. On this account the social meaning of a topos, as conveyed in a argument, is based on the set of idealogically-related topoi it indicates in context. We draw a connection between the role of personae in social meaning and the category adjustment effect, a well-known psychological phenomenon in which the representation of a stimulus is biased in the direction of the category in which it falls. Finally, we situate the interpretation of social signals as an update to the information state of an agent in a formal TTR model of dialogue.
Dogwhistles as Identity-based interpretative variation (2020)

The following paper presents a formal model for the description of \textit{dogwhistles}. Dogwhistles are a class of terms or expressions often used in political discourse that are used with the goal of being interpreted in different ways by different communities. The model presented here describes this phenomenon using a variation on the Social Meaning Games framework that uses probability distributions over possible interpretation functions as well as RSA/IBR reasoning.
Conditional answers and the role of probabilistic epistemic representations (2020)

Conditional utterances can be used in discourse as answers to regular, non-conditional questions in situations of partial knowledge of the answerer. We claim that the probabilities assigned to possible epistemic states of A are a measure of the utility of conditional answers. A second criterion that makes a conditional answer {\textquoteleft}if p, then q' relevant has to do with the dependency between p and q that is conveyed in the statement. A conditional answer counts as relevant when this dependency leads the question asker to shift from a decision problem about q to an alternative, easier, decision problem about p.
Linguistic interpretation as inference under argument system uncertainty: the case of epistemic must (2020)

Modern semantic analyses of epistemic language (incl. the modals must and might) can be characterized by the following {\textquoteleft}credence assumption': speakers have full certainty regarding the propositions that structure their epistemic state. Intuitively, however: a) speakers have graded, rather than categorical, commitment to these propositions, which are often never fully and explicitly articulated; b) listeners have higher-order uncertainty about this speaker uncertainty; c) must p is used to communicate speaker commitment to some conclusion p and to indicate speaker commitment to the premises that condition the conclusion. I explore the consequences of relaxing the credence assumption by extending the argument system semantic framework first proposed by Stone (1994) to a Bayesian probabilistic framework of modeling pragmatic interpretation (Goodman and Frank, 2016). The analysis makes desirable predictions regarding the behavior and interpretation of must, and it suggests a new way of considering the nature of context and communicative exchange.
Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics (2020)

Functional Distributional Semantics provides a computationally tractable framework for learning truth-conditional semantics from a corpus. Previous work in this framework has provided a probabilistic version of first-order logic, recasting quantification as Bayesian inference. In this paper, I show how the previous formulation gives trivial truth values when a precise quantifier is used with vague predicates. I propose an improved account, avoiding this problem by treating a vague predicate as a distribution over precise predicates. I connect this account to recent work in the Rational Speech Acts framework on modelling generic quantification, and I extend this to modelling donkey sentences. Finally, I explain how the generic quantifier can be both pragmatically complex and yet computationally simpler than precise quantifiers.
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot (2020)

The major shortcomings of using neural networks with situated agents are that in incremental interaction very few learning examples are available and that their visual sensory representations are quite different from image caption datasets. In this work we adapt and evaluate a few-shot learning approach, Matching Networks (Vinyals et al., 2016), to conversational strategies of a robot interacting with a human tutor in order to efficiently learn to categorise objects that are presented to it and also investigate to what degree transfer learning from pre-trained models on images from different contexts can improve its performance. We discuss the implications of such learning on the nature of semantic representations the system has learned.
Discrete and Probabilistic Classifier-based Semantics (2020)

We present a formal semantics (a version of Type Theory with Records) which places classifiers of perceptual information at the core of semantics. Using this framework, we present an account of the interpretation and classification of utterances referring to perceptually available situations (such as visual scenes). The account improves on previous work by clarifying the role of classifiers in a hybrid semantics combining statistical/neural classifiers with logical/inferential aspects of meaning. The account covers both discrete and probabilistic classification, thereby enabling learning, vagueness and other non-discrete linguistic phenomena.
Social Meaning in Repeated Interactions (2020)

Judgements about communicative agents evolve over the course of interactions both in how individuals are judged for testimonial reliability and for (ideological) trustworthiness. This paper combines a theory of social meaning and persona with a theory of reliability within a game-theoretic view of communication, giving a formal model involving interactional histories, repeated game models and ways of evaluating social meaning and trustworthiness.
Towards functional, agent-based models of dogwhistle communication (2020)

Henderson and McCready 2017, 2018, 2019 build a novel theory of so-called {\textquoteleft}dogwhistle' communication by extending the social meaning games of Burnett 2017. This work reports on an ongoing project to build systems to model the evolution of dogwhistle communication in a population based on probability monads (Erwig and Kollmansberger, 2006; Kidd, 2007). The ultimate results will be useful not just for dogwhistles, but modeling the diffusion and evolution of social meaning in populations in general. The initial results presented here is a computational implementation of Henderson and McCready 2018, which will serve as the basis for models with multiple speakers and repeated interactions.
Stochastic Frames (2020)

In the frame hypothesis (CITATION), human concepts are equated with frames, which extend feature lists by a functional structure consisting of attributes and values. For example, a bachelor is represented by the attributes gender and marital status and their values {\textquoteleft}male' and {\textquoteleft}unwed'. This paper makes the point that for many applications of concepts in cognition, including for concepts to be associated with lexemes in natural languages, the right structures to assume are not merely frames but stochastic frames in which attributes are associated with probability distributions over values. The paper introduces the idea of stochastic frames and suggests three applications: vagueness, ambiguity, and typicality.
A toy distributional model for fuzzy generalised quantifiers (2020)

Recent work in compositional distributional semantics showed how bialgebras model generalised quantifiers of natural language. That technique requires working with vector space over power sets of bases, and therefore is computationally costly. It is possible to overcome the computational hurdles by working with fuzzy generalised quantifiers. In this paper, we show that the compositional notion of semantics of natural language, guided by a grammar, extends from a binary to a many valued setting and instantiate in it the fuzzy computations. We import vector representations of words and predicates, learnt from large scale compositional distributional semantics, interpret them as fuzzy sets, and analyse their performance on a toy inference dataset.
Generating Lexical Representations of Frames using Lexical Substitution (2020)

Semantic frames are formal linguistic structures describing situations/actions/events, e.g. Commercial transfer of goods. Each frame provides a set of roles corresponding to the situation participants, e.g. Buyer and Goods, and lexical units (LUs) {--} words and phrases that can evoke this particular frame in texts, e.g. Sell. The scarcity of annotated resources hinders wider adoption of frame semantics across languages and domains. We investigate a simple yet effective method, lexical substitution with word representation models, to automatically expand a small set of frame-annotated sentences with new words for their respective roles and LUs. We evaluate the expansion quality using FrameNet. Contextualized models demonstrate overall superior performance compared to the non-contextualized ones on roles. However, the latter show comparable performance on the task of LU expansion.
Informativity in Image Captions vs. Referring Expressions (2020)

At the intersection between computer vision and natural language processing, there has been recent progress on two natural language generation tasks: Dense Image Captioning and Referring Expression Generation for objects in complex scenes. The former aims to provide a caption for a specified object in a complex scene for the benefit of an interlocutor who may not be able to see it. The latter aims to produce a referring expression that will serve to identify a given object in a scene that the interlocutor can see. The two tasks are designed for different assumptions about the common ground between the interlocutors, and serve very different purposes, although they both associate a linguistic description with an object in a complex scene. Despite these fundamental differences, the distinction between these two tasks is sometimes overlooked. Here, we undertake a side-by-side comparison between image captioning and reference game human datasets and show that they differ systematically with respect to informativity. We hope that an understanding of the systematic differences among these human datasets will ultimately allow them to be leveraged more effectively in the associated engineering tasks.
How does Punctuation Affect Neural Models in Natural Language Inference (2020)

Natural Language Inference models have reached almost human-level performance but their generalisation capabilities have not been yet fully characterized. In particular, sensitivity to small changes in the data is a current area of investigation. In this paper, we focus on the effect of punctuation on such models. Our findings can be broadly summarized as follows: (1) irrelevant changes in punctuation are correctly ignored by the recent transformer models (BERT) while older RNN-based models were sensitive to them. (2) All models, both transformers and RNN-based models, are incapable of taking into account small relevant changes in the punctuation.
Building a Swedish Question-Answering Model (2020)

High quality datasets for question answering exist in a few languages, but far from all. Producing such datasets for new languages requires extensive manual labour. In this work we look at different methods for using existing datasets to train question-answering models in languages lacking such datasets. We show that machine translation followed by cross-lingual projection is a viable way to create a full question-answering dataset in a new language. We introduce new methods both for bitext alignment, using optimal transport, and for direct cross-lingual projection, utilizing multilingual BERT. We show that our methods produce good Swedish question-answering models without any manual work. Finally, we apply our proposed methods on Spanish and evaluate it on the XQuAD and MLQA benchmarks where we achieve new state-of-the-art values of 80.4 F1 and 62.9 Exact Match (EM) points on the Spanish XQuAD corpus and 70.8 F1 and 53.0 EM on the Spanish MLQA corpus, showing that the technique is readily applicable to other languages.