pwc-archive/papers-with-abstracts huggingface dataset:
Agent-Based Dynamic Collaboration Support in a Smart Office Space
Authors: Yansen Wang, R. Charles Murray, Haogang Bao, Carolyn Rose
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments. A series of experimental studies has provided an empirical foundation for the design of chat-based conversational agents that significantly improve learning over no-support control conditions and static-support control conditions. In this demo, we expand upon this foundation, bringing conversational agents to structure group interaction into physical spaces, with the specific goal of facilitating collaboration and learning in workplace scenarios.
A Sequence-to-sequence Approach for Numerical Slot-filling Dialog Systems
Authors: Hongjie Shi
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Question Generation, Question-Generation, slot-filling, Slot Filling

Dialog systems capable of filling slots with numerical values have wide applicability to many task-oriented applications. In this paper, we perform a particular case study on the “number_of_guests” slot-filling in hotel reservation domain, and propose two methods to improve current dialog system model on 1. numerical reasoning performance by training the model to predict arithmetic expressions, and 2. multi-turn question generation by introducing additional context slots. Furthermore, because the proposed methods are all based on an end-to-end trainable sequence-to-sequence (seq2seq) neural model, it is possible to achieve further performance improvement on increasing dialog logs in the future.
Multi-Action Dialog Policy Learning with Interactive Human Teaching
Authors: Megha Jhunjhunwala, Caleb Bryant, Pararth Shah
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Imitation Learning, Transfer Learning

We present a framework for improving task-oriented dialog systems through online interactive teaching with human trainers. A dialog policy trained with imitation learning on a limited corpus may not generalize well to novel dialog flows often uncovered in live interactions. This issue is magnified in multi-action dialog policies which have a more expressive action space. In our approach, a pre-trained dialog policy model interacts with human trainers, and at each turn the trainers choose the best output among N-best multi-action outputs. We present a novel multi-domain, multi-action dialog policy architecture trained on MultiWOZ, and show that small amounts of online supervision can lead to significant improvement in model performance. We also present transfer learning results which show that interactive learning in one domain improves policy model performance in related domains.
Similarity Scoring for Dialogue Behaviour Comparison
Authors: Stefan Ultes, Wolfgang Maier
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Decision Making

The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.
Collection and Analysis of Dialogues Provided by Two Speakers Acting as One
Authors: Tsunehiro Arimoto, Ryuichiro Higashinaka, Kou Tanaka, Takahito Kawanishi, Hiroaki Sugiyama, Hiroshi Sawada, Hiroshi Ishiguro
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7

We are studying a cooperation style where multiple speakers can provide both advanced dialogue services and operator education. We focus on a style in which two operators interact with a user by pretending to be a single operator. For two operators to effectively act as one, each must adjust his/her conversational content and timing to the other. In the process, we expect each operator to experience the conversational content of his/her partner as if it were his/her own, creating efficient and effective learning of the other’s skill. We analyzed this educational effect and examined whether dialogue services can be successfully provided by collecting travel guidance dialogue data from operators who give travel information to users. In this paper, we report our preliminary results on dialogue content and user satisfaction of operators and users.
Dialogue Policies for Learning Board Games through Multimodal Communication
Authors: Maryam Zare, Ali Ayub, Aishan Liu, Sweekar Sudhakara, Alan Wagner, Rebecca Passonneau
Date: 2020-07-01 00:00:00
Proceeding: SIGDIAL (ACL) 2020 7
Tasks: Board Games, Informativeness

This paper presents MDP policy learning for agents to learn strategic behavior–how to play board games–during multimodal dialogues. Policies are trained offline in simulation, with dialogues carried out in a formal language. The agent has a temporary belief state for the dialogue, and a persistent knowledge store represented as an extensive-form game tree. How well the agent learns a new game from a dialogue with a simulated partner is evaluated by how well it plays the game, given its dialogue-final knowledge state. During policy training, we control for the simulated dialogue partner’s level of informativeness in responding to questions. The agent learns best when its trained policy matches the current dialogue partner’s informativeness. We also present a novel data collection for training natural language modules. Human subjects who engaged in dialogues with a baseline system rated the system’s language skills as above average. Further, results confirm that human dialogue partners also vary in their informativeness.
A Continuation Semantics for Abstract Meaning Representation
Authors: Kenneth Lai, Lucia Donatelli, James Pustejovsky
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Abstract Meaning Representation, Form, Negation, Translation

Abstract Meaning Representation (AMR) is a simple, expressive semantic framework whose emphasis on predicate-argument structure is effective for many tasks. Nevertheless, AMR lacks a systematic treatment of projection phenomena, making its translation into logical form problematic. We present a translation function from AMR to first order logic using continuation semantics, which allows us to capture the semantic context of an expression in the form of an argument. This is a natural extension of AMR’s original design principles, allowing us to easily model basic projection phenomena such as quantification and negation as well as complex phenomena such as bound variables and donkey anaphora.
Building Korean Abstract Meaning Representation Corpus
Authors: Hyonsu Choe, Jiyoon Han, Hyejin Park, Tae Hwan Oh, Hansaem Kim
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Abstract Meaning Representation

To explore the potential sembanking in Korean and ways to represent the meaning of Korean sentences, this paper reports on the process of applying Abstract Meaning Representation to Korean, a semantic representation framework that has been studied in wide range of languages, and its output: the Korean AMR corpus. The corpus which is constructed so far is a size of 1,253 sentences and its raw texts are from ExoBrain Corpus, a state-led R&D project on language AI. This paper also analyzes the result in both qualitative and quantitative manners, proposing discussions for further development.
Cross-lingual annotation: a road map for low- and no-resource languages
Authors: Meagan Vigus, Jens E. L. Van Gysel, Tim O’Gorman, Andrew Cowell, Rosa Vallejos, William Croft
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Sentence

This paper presents a “road map” for the annotation of semantic categories in typologically diverse languages, with potentially few linguistic resources, and often no existing computational resources. Past semantic annotation efforts have focused largely on high-resource languages, or relatively low-resource languages with a large number of native speakers. However, there are certain typological traits, namely the synthesis of multiple concepts into a single word, that are more common in languages with a smaller speech community. For example, what is expressed as a sentence in a more analytic language like English, may be expressed as a single word in a more synthetic language like Arapaho. This paper proposes solutions for annotating analytic and synthetic languages in a comparable way based on existing typological research, and introduces a road map for the annotation of languages with a dearth of resources.
K-SNACS: Annotating Korean Adposition Semantics
Authors: Jena D. Hwang, Hanwool Choe, Na-Rae Han, Nathan Schneider
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Sentence

While many languages use adpositions to encode semantic relationships between content words in a sentence (e.g., agentivity or temporality), the details of how adpositions work vary widely across languages with respect to both form and meaning. In this paper, we empirically adapt the SNACS framework (Schneider et al., 2018) to Korean, a language that is typologically distant from English—the language SNACS was based on. We apply the SNACS framework to annotate the highly popular novellaThe Little Prince with semantic supersense labels over allKorean postpositions. Thus, we introduce the first broad-coverage corpus annotated with Korean postposition semantics and provide a detailed analysis of the corpus with an apples-to-apples comparison between Korean and English annotations
InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research
Authors: Claire Bonial, Stephanie M. Lukin, David Doughty, Steven Hill, Clare Voss
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Abstract Meaning Representation, Domain Adaptation

This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task.
Semantic parsing with fuzzy meaning representations
Authors: Pavlo Kapustin, Michael Kapustin
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12
Tasks: Semantic Parsing

We propose an approach and a software framework for semantic parsing of natural language sentences to discourse representation structures with use of fuzzy meaning representations such as fuzzy sets and compatibility intervals. We explain the motivation for using fuzzy meaning representations in semantic parsing and describe the design of the proposed approach and the software framework, discussing various examples. We argue that the use of fuzzy meaning representations have potential to improve understanding and reasoning capabilities of systems working with natural language.
Representing constructional metaphors
Authors: Pavlina Kalm, Michael Regan, Sook-kyung Lee, Chris Peverada, William Croft
Date: 2020-12-01 00:00:00
Proceeding: DMR (COLING) 2020 12

This paper introduces a representation and annotation scheme for argument structure constructions that are used metaphorically with verbs in different semantic domains. We aim to contribute to the study of constructional metaphors which has received little attention in theoretical and computational linguistics. The proposed representation consists of a systematic mapping between the constructional and verbal event structures in two domains. It reveals the semantic motivations that lead to constructions being metaphorically extended. We demonstrate this representation on argument structure constructions with Transfer of Possession verbs and test the viability of this scheme with an annotation exercise.
Improving Hate Speech Type and Target Detection with Hateful Metaphor Features
Authors: Jens Lemmens, Ilia Markov, Walter Daelemans
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Vocal Bursts Type Prediction

We study the usefulness of hateful metaphorsas features for the identification of the type and target of hate speech in Dutch Facebook comments. For this purpose, all hateful metaphors in the Dutch LiLaH corpus were annotated and interpreted in line with Conceptual Metaphor Theory and Critical Metaphor Analysis. We provide SVM and BERT/RoBERTa results, and investigate the effect of different metaphor information encoding methods on hate speech type and target detection accuracy. The results of the conducted experiments show that hateful metaphor features improve model performance for the both tasks. To our knowledge, it is the first time that the effectiveness of hateful metaphors as an information source for hatespeech classification is investigated.
Improving Cross-Domain Hate Speech Detection by Reducing the False Positive Rate
Authors: Ilia Markov, Walter Daelemans
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Blocking, Deep Learning, Hate Speech Detection

Hate speech detection is an actively growing field of research with a variety of recently proposed approaches that allowed to push the state-of-the-art results. One of the challenges of such automated approaches – namely recent deep learning models – is a risk of false positives (i.e., false accusations), which may lead to over-blocking or removal of harmless social media content in applications with little moderator intervention. We evaluate deep learning models both under in-domain and cross-domain hate speech detection conditions, and introduce an SVM approach that allows to significantly improve the state-of-the-art results when combined with the deep learning models through a simple majority-voting ensemble. The improvement is mainly due to a reduction of the false positive rate.
Understanding the Impact of Evidence-Aware Sentence Selection for Fact Checking
Authors: Giannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Claim Verification, Fact Checking, Retrieval, Sentence, Sentence Retrieval

Fact Extraction and VERification (FEVER) is a recently introduced task that consists of the following subtasks (i) document retrieval, (ii) sentence retrieval, and (iii) claim verification. In this work, we focus on the subtask of sentence retrieval. Specifically, we propose an evidence-aware transformer-based model that outperforms all other models in terms of FEVER score by using a subset of training instances. In addition, we conduct a large experimental study to get a better understanding of the problem, while we summarize our findings by presenting future research challenges.
Generalisability of Topic Models in Cross-corpora Abusive Language Detection
Authors: Tulika Bose, Irina Illina, Dominique Fohr
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Abuse Detection, Abusive Language, Topic Models

Rapidly changing social media content calls for robust and generalisable abuse detection models. However, the state-of-the-art supervised models display degraded performance when they are evaluated on abusive comments that differ from the training corpus. We investigate if the performance of supervised models for cross-corpora abuse detection can be improved by incorporating additional information from topic models, as the latter can infer the latent topic mixtures from unseen samples. In particular, we combine topical information with representations from a model tuned for classifying abusive comments. Our performance analysis reveals that topic models are able to capture abuse-related topics that can transfer across corpora, and result in improved generalisability.
MEAN: Multi-head Entity Aware Attention Networkfor Political Perspective Detection in News Media
Authors: Chang Li, Dan Goldwasser
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Articles

The way information is generated and disseminated has changed dramatically over the last decade. Identifying the political perspective shaping the way events are discussed in the media becomes more important due to the sharp increase in the number of news outlets and articles. Previous approaches usually only leverage linguistic information. However, news articles attempt to maintain credibility and seem impartial. Therefore, bias is introduced in subtle ways, usually by emphasizing different aspects of the story. In this paper, we propose a novel framework that considers entities mentioned in news articles and external knowledge about them, capturing the bias with respect to those entities. We explore different ways to inject entity information into the text model. Experiments show that our proposed framework achieves significant improvements over the standard text models, and is capable of identifying the difference in news narratives with different perspectives.
An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News
Authors: Chaoyuan Zuo, Qi Zhang, Ritwik Banerjee
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Articles, Fact Checking, Misinformation, News Classification

The explosion of online health news articles runs the risk of the proliferation of low-quality information. Within the existing work on fact-checking, however, relatively little attention has been paid to medical news. We present a health news classification task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists. We present a dataset of 1,119 health news paired with systematic reviews. The review criteria consist of six elements that are essential to the accuracy of medical news. We then present experiments comparing the classical token-based approach with the more recent transformer-based models. Our results show that detecting qualitative lapses is a challenging task with direct ramifications in misinformation, but is an important direction to pursue beyond assigning True or False labels to short claims.
DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT
Authors: Ahmad Hussein, Nada Ghneim, Ammar Joukhadar
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Language Modeling, Language Modelling

The objective of this work was the introduction of an effective approach based on the AraBERT language model for fighting Tweets COVID-19 Infodemic. It was arranged in the form of a two-step pipeline, where the first step involved a series of pre-processing procedures to transform Twitter jargon, including emojis and emoticons, into plain text, and the second step exploited a version of AraBERT, which was pre-trained on plain text, to fine-tune and classify the tweets with respect to their Label. The use of language models pre-trained on plain texts rather than on tweets was motivated by the necessity to address two critical issues shown by the scientific literature, namely (1) pre-trained language models are widely available in many languages, avoiding the time-consuming and resource-intensive model training directly on tweets from scratch, allowing to focus only on their fine-tuning; (2) available plain text corpora are larger than tweet-only ones, allowing for better performance.
NARNIA at NLP4IF-2021: Identification of Misinformation in COVID-19 Tweets Using BERTweet
Authors: Ankit Kumar, Naman Jhunjhunwala, Raksha Agarwal, Niladri Chatterjee
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Misinformation, Word Embeddings

The spread of COVID-19 has been accompanied with widespread misinformation on social media. In particular, Twitterverse has seen a huge increase in dissemination of distorted facts and figures. The present work aims at identifying tweets regarding COVID-19 which contains harmful and false information. We have experimented with a number of Deep Learning-based models, including different word embeddings, such as Glove, ELMo, among others. BERTweet model achieved the best overall F1-score of 0.881 and secured the third rank on the above task.
R00 at NLP4IF-2021 Fighting COVID-19 Infodemic with Transformers and More Transformers
Authors: Ahmed Qarqaz, Dia Abujaber, Malak Abdullah
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6

This paper describes the winning model in the Arabic NLP4IF shared task for fighting the COVID-19 infodemic. The goal of the shared task is to check disinformation about COVID-19 in Arabic tweets. Our proposed model has been ranked 1st with an F1-Score of 0.780 and an Accuracy score of 0.762. A variety of transformer-based pre-trained language models have been experimented with through this study. The best-scored model is an ensemble of AraBERT-Base, Asafya-BERT, and ARBERT models. One of the study’s key findings is showing the effect the pre-processing can have on every model’s score. In addition to describing the winning model, the current study shows the error analysis.
Multi Output Learning using Task Wise Attention for Predicting Binary Properties of Tweets : Shared-Task-On-Fighting the COVID-19 Infodemic
Authors: Ayush Suhane, Shreyas Kowshik
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Position

In this paper, we describe our system for the shared task on Fighting the COVID-19 Infodemic in the English Language. Our proposed architecture consists of a multi-output classification model for the seven tasks, with a task-wise multi-head attention layer for inter-task information aggregation. This was built on top of the Bidirectional Encoder Representations obtained from the RoBERTa Transformer. We were able to achieve a mean F1 score of 0.891 on the test data, leading us to the second position on the test-set leaderboard.
Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings
Authors: Subhadarshi Panda, Sarah Ita Levitan
Date: 2021-06-01 00:00:00
Proceeding: NAACL (NLP4IF) 2021 6
Tasks: Misinformation

We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic. We compared 4 multitask learning models for this task and found that a model trained with English BERT achieves the best results for English, and multilingual BERT achieves the best results for Bulgarian and Arabic. We experimented with zero shot, few shot, and target-only conditions to evaluate the impact of target-language training data on classifier performance, and to understand the capabilities of different models to generalize across languages in detecting misinformation online. This work was performed as a submission to the shared task, NLP4IF 2021: Fighting the COVID-19 Infodemic. Our best models achieved the second best evaluation test results for Bulgarian and Arabic among all the participating teams and obtained competitive scores for English.