Michal Jacovi


2020

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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.

2019

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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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Automatic Argument Quality Assessment - New Datasets and Methods
Assaf Toledo | Shai Gretz | Edo Cohen-Karlik | Roni Friedman | Elad Venezian | Dan Lahav | 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)

We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.

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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.

2018

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A Recorded Debating Dataset
Shachar Mirkin | Michal Jacovi | Tamar Lavee | Hong-Kwang Kuo | Samuel Thomas | Leslie Sager | Lili Kotlerman | Elad Venezian | Noam Slonim
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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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.