pwc-archive/papers-with-abstracts huggingface dataset:
Visually Grounded Follow-up Questions: a Dataset of Spatial Questions Which Require Dialogue History
Authors: Tianai Dong, Alberto Testoni, Luciana Benotti, Raffaella Bernardi
Date: 2021-08-01 00:00:00
Proceeding: ACL (splurobonlp) 2021 8

In this paper, we define and evaluate a methodology for extracting history-dependent spatial questions from visual dialogues. We say that a question is history-dependent if it requires (parts of) its dialogue history to be interpreted. We argue that some kinds of visual questions define a context upon which a follow-up spatial question relies. We call the question that restricts the context: trigger, and we call the spatial question that requires the trigger question to be answered: zoomer. We automatically extract different trigger and zoomer pairs based on the visual property that the questions rely on (e.g. color, number). We manually annotate the automatically extracted trigger and zoomer pairs to verify which zoomers require their trigger. We implement a simple baseline architecture based on a SOTA multimodal encoder. Our results reveal that there is much room for improvement for answering history-dependent questions.
Modeling Semantics and Pragmatics of Spatial Prepositions via Hierarchical Common-Sense Primitives
Authors: Georgiy Platonov, Yifei Yang, HaoYu Wu, Jonathan Waxman, Marcus Hill, Lenhart Schubert
Date: 2021-08-01 00:00:00
Proceeding: ACL (splurobonlp) 2021 8
Tasks: Common Sense Reasoning

Understanding spatial expressions and using them appropriately is necessary for seamless and natural human-machine interaction. However, capturing the semantics and appropriate usage of spatial prepositions is notoriously difficult, because of their vagueness and polysemy. Although modern data-driven approaches are good at capturing statistical regularities in the usage, they usually require substantial sample sizes, often do not generalize well to unseen instances and, most importantly, their structure is essentially opaque to analysis, which makes diagnosing problems and understanding their reasoning process difficult. In this work, we discuss our attempt at modeling spatial senses of prepositions in English using a combination of rule-based and statistical learning approaches. Each preposition model is implemented as a tree where each node computes certain intuitive relations associated with the preposition, with the root computing the final value of the prepositional relation itself. The models operate on a set of artificial 3D “room world” environments, designed in Blender, taking the scene itself as an input. We also discuss our annotation framework used to collect human judgments employed in the model training. Both our factored models and black-box baseline models perform quite well, but the factored models will enable reasoned explanations of spatial relation judgements.
Plan Explanations that Exploit a Cognitive Spatial Model
Authors: Raj Korpan, Susan L. Epstein
Date: 2021-08-01 00:00:00
Proceeding: ACL (splurobonlp) 2021 8
Tasks: model, Navigate

Ideally, people who navigate together in a complex indoor space share a mental model that facilitates explanation. This paper reports on a robot control system whose cognitive world model is based on spatial affordances that generalize over its perceptual data. Given a target, the control system formulates multiple plans, each with a model-relevant metric, and selects among them. As a result, it can provide readily understandable natural language about the robot’s intentions and confidence, and generate diverse, contrastive explanations that reference the acquired spatial model. Empirical results in large, complex environments demonstrate the robot’s ability to provide human-friendly explanations in natural language.
Interactive Reinforcement Learning for Table Balancing Robot
Authors: Haein Jeon, Yewon Kim, Bo-Yeong Kang
Date: 2021-08-01 00:00:00
Proceeding: ACL (splurobonlp) 2021 8
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentiment Analysis, speech-recognition, Speech Recognition

With the development of robotics, the use of robots in daily life is increasing, which has led to the need for anyone to easily train robots to improve robot use. Interactive reinforcement learning(IARL) is a method for robot training based on human–robot interaction; prior studies on IARL provide only limited types of feedback or require appropriately designed shaping rewards, which is known to be difficult and time-consuming. Therefore, in this study, we propose interactive deep reinforcement learning models based on voice feedback. In the proposed system, a robot learns the task of cooperative table balancing through deep Q-network using voice feedback provided by humans in real-time, with automatic speech recognition(ASR) and sentiment analysis to understand human voice feedback. As a result, an optimal policy convergence rate of up to 96% was realized, and performance was improved in all voice feedback-based models
Multi-Level Gazetteer-Free Geocoding
Authors: Sayali Kulkarni, Shailee Jain, Mohammad Javad Hosseini, Jason Baldridge, Eugene Ie, Li Zhang
Date: 2021-08-01 00:00:00
Proceeding: ACL (splurobonlp) 2021 8
Tasks: Toponym Resolution

We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates. The Earth’s surface is represented using space-filling curves that decompose the sphere into a hierarchical grid. MLG balances classification granularity and accuracy by combining losses across multiple levels and jointly predicting cells at different levels simultaneously. It obtains large gains without any gazetteer metadata, demonstrating that it can effectively learn the connection between text spans and coordinates—and thus makes it a gazetteer-free geocoder. Furthermore, MLG obtains state-of-the-art results for toponym resolution on three English datasets without any dataset-specific tuning.
Modality: logic, semantics, annotation and machine learning
Authors: Annie Zaenen
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9
Tasks: BIG-bench Machine Learning, Information Retrieval, Natural Language Understanding, Retrieval

Up to rather recently Natural Language Processing has not given much attention to modality. As long as the main task was to determined what a text was about (Information Retrieval) or who the participants in an eventuality were (Information Extraction), this neglect was understandable. With the focus moving to questions of natural language understanding and inferencing as well as to sentiment and opinion analysis, it becomes necessary to distinguish between actual and envisioned eventualities and to draw conclusions about the attitude of the writer or speaker towards the eventualities referred to. This means, i.a., to be able to distinguish ‘John went to Paris’ and ‘John wanted to go to Paris’. To do this one has to calculate the effect of different linguistic operators on the eventuality predication.
Modal Subordination in Type Theoretic Dynamic Logic
Authors: Sai Qian, Philippe de Groote, Maxime Amblard
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9
Tasks: Sentence, Vocal Bursts Type Prediction

Classical theories of discourse semantics, such as Discourse Representation Theory (DRT), Dynamic Predicate Logic (DPL), predict that an indefinite noun phrase cannot serve as antecedent for an anaphor if the noun phrase is, but the anaphor is not, in the scope of a modal expression. However, this prediction meets with counterexamples. The phenomenon modal subordination is one of them. In general, modal subordination is concerned with more than two modalities, where the modality in subsequent sentences is interpreted in a context ‘subordinate’ to the one created by the first modal expression. In other words, subsequent sentences are interpreted as being conditional on the scenario introduced in the first sentence. One consequence is that the anaphoric potential of indefinites may extend beyond the standard limits of accessibility constraints. This paper aims to give a formal interpretation on modal subordination. The theoretical backbone of the current work is Type Theoretic Dynamic Logic (TTDL), which is a Montagovian account of discourse semantics. Different from other dynamic theories, TTDL was built on classical mathematical and logical tools, such as λ-calculus and Church’s theory of types. Hence it is completely compositional and does not suffer from the destructive assignment problem. We will review the basic set-up of TTDL and then present Kratzer’s theory on natural language modality. After that, by integrating the notion of conversation background, in particular, the modal base usage, we offer an extension of TTDL (called Modal-TTDL, or M-TTDL in short) which properly deals with anaphora across modality. The formal relation between Modal-TTDL and TTDL will be discussed as well. We uncover the difficulty of specific sense distinctions by investigating distributional bias and reducing the sparsity of existing small-scale corpora used in prior work. We build a semantically enriched model for modal sense classification by designing novel features related to lexical, proposition-level and discourse-level semantic factors. Besides improved classification performance, closer examination of interpretable feature sets unveils relevant semantic and contextual factors in modal sense classification. Finally, we investigate genre effects on modal sense distribution and how they affect classification performance. Our investigations uncover the difficulty of specific sense distinctions and how they are affected by training set size and distributional bias. Our large-scale experiments confirm that semantically enriched models outperform models built on shallow feature sets. Cross-genre experiments shed light on differences in sense distributions across genres and confirm that semantically enriched models have high generalization capacity, especially in unstable distributional settings.
Modal Sense Classification At Large: Paraphrase-Driven Sense Projection, Semantically Enriched Classification Models and Cross-Genre Evaluations
Authors: Ana Marasović, Mengfei Zhou, Alexis Palmer, Anette Frank
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9
Tasks: Classification

Modal verbs have different interpretations depending on their context. Their sense categories – epistemic, deontic and dynamic – provide important dimensions of meaning for the interpretation of discourse. Previous work on modal sense classification achieved relatively high performance using shallow lexical and syntactic features drawn from small-size annotated corpora. Due to the restricted empirical basis, it is difficult to assess the particular difficulties of modal sense classification and the generalization capacity of the proposed models. In this work we create large-scale, high-quality annotated corpora for modal sense classification using an automatic paraphrase-driven projection approach. Using the acquired corpora, we investigate the modal sense classification task from different perspectives.
A linguistically-motivated annotation model of modality in English and Spanish: Insights from MULTINOT
Authors: Julia Lavid, Marta Carretrero, Juan Rafael Zamorano-Mansilla
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9

In this paper we present current work on the design and validation of a linguistically-motivated annotation model of modality in English and Spanish in the context of the MULTINOT project. Our annotation model captures four basic modal meanings and their subtypes, on the one hand, and provides a fine-grained characterisation of the syntactic realisations of those meanings in English and Spanish, on the other. We validate the modal tagset proposed through an agreement study performed on a bilingual sample of four hundred sentences extracted from original texts of the MULTINOT corpus, and discuss the difficult cases encountered in the annotation experiment. We also describe current steps in the implementation of the proposed scheme for the large-scale annotation of the bilingual corpus using both automatic and manual procedures.
Modality annotation for Portuguese: from manual annotation to automatic labeling
Authors: Amália Mendes, Iris Hendrickx, Liciana Ávila, Paulo Quaresma, Teresa Gonҫalves, João Sequeira
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9
Tasks: Negation

We investigate modality in Portuguese and we combine a linguistic perspective with an application-oriented perspective on modality. We design an annotation scheme reflecting theoretical linguistic concepts and apply this schema to a small corpus sample to show how the scheme deals with real world language usage. We present two schemas for Portuguese, one for spoken Brazilian Portuguese and one for written European Portuguese. Furthermore, we use the annotated data not only to study the linguistic phenomena of modality, but also to train a practical text mining tool to detect modality in text automatically. The modality tagger uses a machine learning classifier trained on automatically extracted features from a syntactic parser. As we only have a small annotated sample available, the tagger was evaluated on 11 modal verbs that are frequent in our corpus and that denote more than one modal meaning. Finally, we discuss several valuable insights into the complexity of the semantic concept of modality that derive from the process of manual annotation of the corpus and from the analysis of the results of the automatic labeling: ambiguity and the semantic and syntactic properties typically associated to one modal meaning in context, and also the interaction of modality with negation and focus. The knowledge gained from the manual annotation task leads us to propose a new unified scheme for modality that applies to the two Portuguese varieties and covers both written and spoken data.
Selective Annotation of Modal Readings: Delving into the Difficult Data
Authors: Lori Moon, Patricija Kirvaitis, Noreen Madden
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9
Tasks: Natural Language Inference, Sentence

Modal auxiliaries have different readings, depending on the context in which they occur (Kratzer, 1981). Several projects have attempted to classify uses of modal auxiliaries in corpora according to their reading using supervised machine learning techniques (e.g., Rubinstein et al., 2013, Ruppenhofer & Rehbein, 2012). In each study, traditional taxonomic labels, such as ‘epistemic’ and ‘deontic’ are used by human annotators to label instances of modal auxiliaries in a corpus. In order to achieve higher agreement among annotators, results in these previous studies are reported after collapsing some of the initial categories. The results show that human annotators have fairly good agreement on some of the categories, such as whether or not a use is epistemic, but poor agreement on others. They also show that annotators agree more on modals such as might than on modals such as could. In this study, we used traditional taxonomic categories on sentences containing modal auxiliary verbs that were randomly extracted from the English Gigaword 4th edition corpus (Parker et al., 2009). The lowest inner-annotator agreement using traditional taxonomic labels occurred with uses of could, with raw agreements of 42%−48% (κ = 0.196−0.259), compared to might, for instance, with raw agreement of 98%. In response to the low numbers, rather than collapsing traditional categories, we tried a new method of classifying uses of could with respect to where the reading situates the eventuality being described relative to the speech time. For example, the sentence ‘Jess could swim.’ is about a swimming eventuality in the past leading up to the time of speech, if it is read as being an ability. The sentence is about a swimming eventuality in the future, if it is read as being a statement about a possibility. The classification labels we propose are crucial in separating uses of could that have actuality inferences (Bhatt, 1999, Hacquard, 2006) from uses that do not. For the temporal location of the event described by a use of could, using four category labels, we achieved 73%−90% raw agreement (κ = 0.614−0.744). Sequence of tense contexts (Abusch, 1997) present a major factor in the difficulty of determining the temporal properties present in uses of could. Among three annotators, we achieved raw agreement scores of 89%−96%(κ =0.779−0.919%) on identification of sequence of tense contexts. We discuss the role of our findings with respect to textual entailment.
Sarcastic Soulmates: Intimacy and irony markers in social media messaging
Authors: Koen Hallmann, Florian Kunneman, Christine Liebrecht, Antal Van den Bosch, Margot van Mulken
Date: 2016-09-01 00:00:00
Proceeding: LILT 2016 9
Tasks: Opinion Mining, Sentiment Analysis

Verbal irony, or sarcasm, presents a significant technical and conceptual challenge when it comes to automatic detection. Moreover, it can be a disruptive factor in sentiment analysis and opinion mining, because it changes the polarity of a message implicitly. Extant methods for automatic detection are mostly based on overt clues to ironic intent such as hashtags, also known as irony markers. In this paper, we investigate whether people who know each other make use of irony markers less often than people who do not know each other. We trained a machinelearning classifier to detect sarcasm in Twitter messages (tweets) that were addressed to specific users, and in tweets that were not addressed to a particular user. Human coders analyzed the top-1000 features found to be most discriminative into ten categories of irony markers. The classifier was also tested within and across the two categories. We find that tweets with a user mention contain fewer irony markers than tweets not addressed to a particular user. Classification experiments confirm that the irony in the two types of tweets is signaled differently. The within-category performance of the classifier is about 91% for both categories, while cross-category experiments yield substantially lower generalization performance scores of 75% and 71%. We conclude that irony markers are used more often when there is less mutual knowledge between sender and receiver. Senders addressing other Twitter users less often use irony markers, relying on mutual knowledge which should lead the receiver to infer ironic intent from more implicit clues. With regard to automatic detection, we conclude that our classifier is able to detect ironic tweets addressed at another user as reliably as tweets that are not addressed at at a particular person.
A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives
Authors: Jiahui Hu, Patrick Paroubek
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

In this paper about aspect-based sentiment analysis (ABSA), we present the first version of a fine-grained annotated corpus for target-based opinion analysis (TBOA) to analyze economic activities or financial markets. We have annotated, at an intra-sentential level, a corpus of sentences extracted from documents representative of financial analysts’ most-read materials by considering how financial actors communicate about the evolution of event trends and analyze related publications (news, official communications, etc.). Since we focus on identifying the expressions of opinions related to the economy and financial markets, we annotated the sentences that contain at least one subjective expression about a domain-specific term. Candidate sentences for annotations were randomly chosen from texts of specialized press and professional information channels over a period ranging from 1986 to 2021. Our annotation scheme relies on various linguistic markers like domain-specific vocabulary, syntactic structures, and rhetorical relations to explicitly describe the author’s subjective stance. We investigated and evaluated the recourse to automatic pre-annotation with existing natural language processing technologies to alleviate the annotation workload. Our aim is to propose a corpus usable on the one hand as training material for the automatic detection of the opinions expressed on an extensive range of domain-specific aspects and on the other hand as a gold standard for evaluation TBOA. In this paper, we present our pre-annotation models and evaluations of their performance, introduce our annotation scheme and report on the main characteristics of our corpus.
The Global Banking Standards QA Dataset (GBS-QA)
Authors: Kyunghwan Sohn, Sunjae Kwon, Jaesik Choi
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Question Answering, Reading Comprehension, Transfer Learning

A domain specific question answering (QA) dataset dramatically improves the machine comprehension performance. This paper presents a new Global Banking Standards QA dataset (GBS-QA) in the banking regulation domain. The GBS-QA has three values. First, it contains actual questions from market players and answers from global rule setter, the Basel Committee on Banking Supervision (BCBS) in the middle of creating and revising banking regulations. Second, financial regulation experts analyze and verify pairs of questions and answers in the annotation process. Lastly, the GBS-QA is a totally different dataset with existing datasets in finance and is applicable to stimulate transfer learning research in the banking regulation domain.
Corporate Bankruptcy Prediction with BERT Model
Authors: Alex Kim, Sangwon Yoon
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Domain Adaptation, Language Modeling, Language Modelling, model, Prediction, Self-Learning, Sentiment Analysis

This study performs BERT-based analysis, which is a representative contextualized language model, on corporate disclosure data to predict impending bankruptcies. Prior literature on bankruptcy prediction mainly focuses on developing more sophisticated prediction methodologies with financial variables. However, in our study, we focus on improving the quality of input dataset. Specifically, we employ BERT model to perform sentiment analysis on MD&A disclosures. We show that BERT outperforms dictionary-based predictions and Word2Vec-based predictions in terms of adjusted R-square in logistic regression, k-nearest neighbor (kNN-5), and linear kernel support vector machine (SVM). Further, instead of pre-training the BERT model from scratch, we apply self-learning with confidence-based filtering to corporate disclosure data (10-K). We achieve the accuracy rate of 91.56% and demonstrate that the domain adaptation procedure brings a significant improvement in prediction accuracy.
Is Domain Adaptation Worth Your Investment? Comparing BERT and FinBERT on Financial Tasks
Authors: Bo Peng, Emmanuele Chersoni, Yu-Yin Hsu, Chu-Ren Huang
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Continual Pretraining, Domain Adaptation

With the recent rise in popularity of Transformer models in Natural Language Processing, research efforts have been dedicated to the development of domain-adapted versions of BERT-like architectures. In this study, we focus on FinBERT, a Transformer model trained on text from the financial domain. By comparing its performances with the original BERT on a wide variety of financial text processing tasks, we found continual pretraining from the original model to be the more beneficial option. Domain-specific pretraining from scratch, conversely, seems to be less effective.