Yonatan Bilu


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Multilingual Argument Mining: Datasets and Analysis
Orith Toledo-Ronen | Matan Orbach | Yonatan Bilu | Artem Spector | Noam Slonim
Findings of the Association for Computational Linguistics: EMNLP 2020

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However, as with many other NLU tasks, the dominant language is English, with resources in other languages being few and far between. In this work, we explore the potential of transfer learning using the multilingual BERT model to address argument mining tasks in non-English languages, based on English datasets and the use of machine translation. We show that such methods are well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments, presumably because quality is harder to preserve under translation. In addition, focusing on the translate-train approach, we show how the choice of languages for translation, and the relations among them, affect the accuracy of the resultant model. Finally, to facilitate evaluation of transfer learning on argument mining tasks, we provide a human-generated dataset with more than 10k arguments in multiple languages, as well as machine translation of the English datasets.

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The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation
Shai Gretz | Yonatan Bilu | Edo Cohen-Karlik | Noam Slonim
Findings of the Association for Computational Linguistics: EMNLP 2020

Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments. In addition, we explore the interplay between this task and the task of Claim Retrieval, showing how they can complement one another.

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Out of the Echo Chamber: Detecting Countering Debate Speeches
Matan Orbach | Yonatan Bilu | Assaf Toledo | Dan Lahav | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in “echo chambers” and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns – that of detecting articles that most effectively counter the arguments – and not just the stance – made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.


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Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining
Tamar Lavee | Matan Orbach | Lili Kotlerman | Yoav Kantor | Shai Gretz | Lena Dankin | Michal Jacovi | Yonatan Bilu | Ranit Aharonov | Noam Slonim
Proceedings of the 6th Workshop on Argument Mining

Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a corpus of news articles containing billions of sentences, and searching for them in a given speech. This raises the question of whether such claims indeed correspond to those made in spoken speeches. To this end, we collected a large dataset of 400 speeches in English discussing 200 controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech. Results show that in the vast majority of speeches debaters indeed make use of such claims. In addition, we present several baselines for the automatic detection of mined claims in speeches, forming the basis for future work. All collected data is freely available for research.

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A Dataset of General-Purpose Rebuttal
Matan Orbach | Yonatan Bilu | Ariel Gera | Yoav Kantor | Lena Dankin | Tamar Lavee | Lili Kotlerman | Shachar Mirkin | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.

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Financial Event Extraction Using Wikipedia-Based Weak Supervision
Liat Ein-Dor | Ariel Gera | Orith Toledo-Ronen | Alon Halfon | Benjamin Sznajder | Lena Dankin | Yonatan Bilu | Yoav Katz | Noam Slonim
Proceedings of the Second Workshop on Economics and Natural Language Processing

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.

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Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation
Tamar Lavee | Lili Kotlerman | Matan Orbach | Yonatan Bilu | Michal Jacovi | Ranit Aharonov | Noam Slonim
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP

Recent advancements in machine reading and listening comprehension involve the annotation of long texts. Such tasks are typically time consuming, making crowd-annotations an attractive solution, yet their complexity often makes such a solution unfeasible. In particular, a major concern is that crowd annotators may be tempted to skim through long texts, and answer questions without reading thoroughly. We present a case study of adapting this type of task to the crowd. The task is to identify claims in a several minute long debate speech. We show that sentence-by-sentence annotation does not scale and that labeling only a subset of sentences is insufficient. Instead, we propose a scheme for effectively performing the full, complex task with crowd annotators, allowing the collection of large scale annotated datasets. We believe that the encountered challenges and pitfalls, as well as lessons learned, are relevant in general when collecting data for large scale natural language understanding (NLU) tasks.

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From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion
Roy Bar-Haim | Dalia Krieger | Orith Toledo-Ronen | Lilach Edelstein | Yonatan Bilu | Alon Halfon | Yoav Katz | Amir Menczel | Ranit Aharonov | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

When debating a controversial topic, it is often desirable to expand the boundaries of discussion. For example, we may consider the pros and cons of possible alternatives to the debate topic, make generalizations, or give specific examples. We introduce the task of Debate Topic Expansion - finding such related topics for a given debate topic, along with a novel annotated dataset for the task. We focus on relations between Wikipedia concepts, and show that they differ from well-studied lexical-semantic relations such as hypernyms, hyponyms and antonyms. We present algorithms for finding both consistent and contrastive expansions and demonstrate their effectiveness empirically. We suggest that debate topic expansion may have various use cases in argumentation mining.

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Argument Invention from First Principles
Yonatan Bilu | Ariel Gera | Daniel Hershcovich | Benjamin Sznajder | Dan Lahav | Guy Moshkowich | Anael Malet | Assaf Gavron | Noam Slonim
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Competitive debaters often find themselves facing a challenging task – how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on ”first principles”, commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a ”first attempt” it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.


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Listening Comprehension over Argumentative Content
Shachar Mirkin | Guy Moshkowich | Matan Orbach | Lili Kotlerman | Yoav Kantor | Tamar Lavee | Michal Jacovi | Yonatan Bilu | Ranit Aharonov | Noam Slonim
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.


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Computational Argumentation Quality Assessment in Natural Language
Henning Wachsmuth | Nona Naderi | Yufang Hou | Yonatan Bilu | Vinodkumar Prabhakaran | Tim Alberdingk Thijm | Graeme Hirst | Benno Stein
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment.


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Claim Synthesis via Predicate Recycling
Yonatan Bilu | Noam Slonim
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


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Automatic Claim Negation: Why, How and When
Yonatan Bilu | Daniel Hershcovich | Noam Slonim
Proceedings of the 2nd Workshop on Argumentation Mining


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Context Dependent Claim Detection
Ran Levy | Yonatan Bilu | Daniel Hershcovich | Ehud Aharoni | Noam Slonim
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Claims on demand – an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora
Noam Slonim | Ehud Aharoni | Carlos Alzate | Roy Bar-Haim | Yonatan Bilu | Lena Dankin | Iris Eiron | Daniel Hershcovich | Shay Hummel | Mitesh Khapra | Tamar Lavee | Ran Levy | Paul Matchen | Anatoly Polnarov | Vikas Raykar | Ruty Rinott | Amrita Saha | Naama Zwerdling | David Konopnicki | Dan Gutfreund
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations