Roy Bar-Haim


2021

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Project Debater APIs: Decomposing the AI Grand Challenge
Roy Bar-Haim | Yoav Kantor | Elad Venezian | Yoav Katz | Noam Slonim
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Project Debater was revealed in 2019 as the first AI system that can debate human experts on complex topics. Engaging in a live debate requires a diverse set of skills, and Project Debater has been developed accordingly as a collection of components, each designed to perform a specific subtask. Project Debater APIs provide access to many of these capabilities, as well as to more recently developed ones. This diverse set of web services, publicly available for academic use, includes core NLP services, argument mining and analysis capabilities, and higher-level services for content summarization. We describe these APIs and their performance, and demonstrate how they can be used for building practical solutions. In particular, we will focus on Key Point Analysis, a novel technology that identifies the main points and their prevalence in a collection of texts such as survey responses and user reviews.

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Every Bite Is an Experience: Key Point Analysis of Business Reviews
Roy Bar-Haim | Lilach Eden | Yoav Kantor | Roni Friedman | Noam Slonim
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.

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Advances in Debating Technologies: Building AI That Can Debate Humans
Roy Bar-Haim | Liat Ein-Dor | Matan Orbach | Elad Venezian | Noam Slonim
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts

The tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as “computational technologies developed directly to enhance, support, and engage with human debating” (Gurevych et al., 2016). A recent milestone in this field is Project Debater, which was revealed in 2019 as the first AI system that can debate human experts on complex topics. Project Debater is the third in the series of IBM Research AI’s grand challenges, following Deep Blue and Watson. It has been developed for over six years by a large team of researchers and engineers, and its live demonstration in February 2019 received massive media attention. This research effort has resulted in more than 50 scientific papers to date, and many datasets freely available for research purposes. We discuss the scientific challenges that arise when building such a system, including argument mining, argument quality assessment, stance classification, principled argument detection, narrative generation, and rebutting a human opponent. Many of the underlying capabilities of Project Debater have been made freely available for academic research, and the tutorial will include a detailed explanation of how to use and leverage these tools. In addition to discussing individual components, the tutorial also provides a holistic view of a debating system. Such a view is largely missing in the academic literature, where each paper typically addresses a specific problem in isolation. We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components. Finally, we discuss practical applications and future challenges of debating technologies.

2020

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From Arguments to Key Points: Towards Automatic Argument Summarization
Roy Bar-Haim | Lilach Eden | Roni Friedman | Yoav Kantor | Dan Lahav | Noam Slonim
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed key points, each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.

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Quantitative argument summarization and beyond: Cross-domain key point analysis
Roy Bar-Haim | Yoav Kantor | Lilach Eden | Roni Friedman | Dan Lahav | Noam Slonim
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

When summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. Work on multi-document summarization has traditionally focused on creating textual summaries, which lack this quantitative aspect. Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments. The current work advances key point analysis in two important respects: first, we develop a method for automatic extraction of key points, which enables fully automatic analysis, and is shown to achieve performance comparable to a human expert. Second, we demonstrate that the applicability of key point analysis goes well beyond argumentation data. Using models trained on publicly available argumentation datasets, we achieve promising results in two additional domains: municipal surveys and user reviews. An additional contribution is an in-depth evaluation of argument-to-key point matching models, where we substantially outperform previous results.

2019

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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.

2018

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SLIDE - a Sentiment Lexicon of Common Idioms
Charles Jochim | Francesca Bonin | Roy Bar-Haim | Noam Slonim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Learning Sentiment Composition from Sentiment Lexicons
Orith Toledo-Ronen | Roy Bar-Haim | Alon Halfon | Charles Jochim | Amir Menczel | Ranit Aharonov | Noam Slonim
Proceedings of the 27th International Conference on Computational Linguistics

Sentiment composition is a fundamental sentiment analysis problem. Previous work relied on manual rules and manually-created lexical resources such as negator lists, or learned a composition function from sentiment-annotated phrases or sentences. We propose a new approach for learning sentiment composition from a large, unlabeled corpus, which only requires a word-level sentiment lexicon for supervision. We automatically generate large sentiment lexicons of bigrams and unigrams, from which we induce a set of lexicons for a variety of sentiment composition processes. The effectiveness of our approach is confirmed through manual annotation, as well as sentiment classification experiments with both phrase-level and sentence-level benchmarks.

2017

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Stance Classification of Context-Dependent Claims
Roy Bar-Haim | Indrajit Bhattacharya | Francesco Dinuzzo | Amrita Saha | Noam Slonim
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Recent work has addressed the problem of detecting relevant claims for a given controversial topic. We introduce the complementary task of Claim Stance Classification, along with the first benchmark dataset for this task. We decompose this problem into: (a) open-domain target identification for topic and claim (b) sentiment classification for each target, and (c) open-domain contrast detection between the topic and the claim targets. Manual annotation of the dataset confirms the applicability and validity of our model. We describe an implementation of our model, focusing on a novel algorithm for contrast detection. Our approach achieves promising results, and is shown to outperform several baselines, which represent the common practice of applying a single, monolithic classifier for stance classification.

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Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization
Roy Bar-Haim | Lilach Edelstein | Charles Jochim | Noam Slonim
Proceedings of the 4th Workshop on Argument Mining

Stance classification is a core component in on-demand argument construction pipelines. Previous work on claim stance classification relied on background knowledge such as manually-composed sentiment lexicons. We show that both accuracy and coverage can be significantly improved through automatic expansion of the initial lexicon. We also developed a set of contextual features that further improves the state-of-the-art for this task.

2016

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Expert Stance Graphs for Computational Argumentation
Orith Toledo-Ronen | Roy Bar-Haim | Noam Slonim
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

2014

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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

2011

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Identifying and Following Expert Investors in Stock Microblogs
Roy Bar-Haim | Elad Dinur | Ronen Feldman | Moshe Fresko | Guy Goldstein
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2009

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A Compact Forest for Scalable Inference over Entailment and Paraphrase Rules
Roy Bar-Haim | Jonathan Berant | Ido Dagan
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Contextual Preferences
Idan Szpektor | Ido Dagan | Roy Bar-Haim | Jacob Goldberger
Proceedings of ACL-08: HLT

2007

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Semantic Inference at the Lexical-Syntactic Level for Textual Entailment Recognition
Roy Bar-Haim | Ido Dagan | Iddo Greental | Idan Szpektor | Moshe Friedman
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

2005

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Choosing an Optimal Architecture for Segmentation and POS-Tagging of Modern Hebrew
Roy Bar-Haim | Khalil Sima’an | Yoad Winter
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages

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Definition and Analysis of Intermediate Entailment Levels
Roy Bar-Haim | Idan Szpektor | Oren Glickman
Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment