Papers by Michal Jacovi
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
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Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
A Dataset of General-Purpose Rebuttal (D19-1)
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Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | a key element in argumentation is rebuttal, the ability to contest an argument by presenting a counter-argument. |
| Approach: | They propose a method based on general rebuttal arguments to produce a critical response to a long argumentative text. |
| Outcome: | The proposed method overcomes the need for topic-specific arguments to be provided . it allows creating responses beyond the scope of topics for which specific arguments are available . |
Listening Comprehension over Argumentative Content (D18-1)
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Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
| Challenge: | In argumentation domain, people are exposed directly to audio (or the video), without access to a written version. |
| Approach: | They present a task for machine listening comprehension in the argumentation domain and a dataset in English. |
| Outcome: | The proposed task is based on 200 speeches arguing for or against 50 controversial topics and uses baseline methods to address it. |
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation (D19-59)
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| Challenge: | Recent advances in machine reading and listening comprehension involve the annotation of long texts. |
| Approach: | They propose a way to perform a sentence-by-sentence annotation task with crowd annotators. |
| Outcome: | The proposed approach can be used to identify claims in a debate speech. |
A Recorded Debating Dataset (L18-1)
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Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
| Challenge: | Existing research in computational argumentation and debating technologies focuses on argumentation mining, but other tasks are being addressed as well. |
| Approach: | They describe a dataset of debating speeches in English that is used for research . they use an automatic speech recognition system to produce a more "nLP-friendly" text . |
| Outcome: | The proposed dataset contains 60 speeches on various controversial topics, each in five formats corresponding to different stages in production. |
Out of the Echo Chamber: Detecting Countering Debate Speeches (2020.acl-main)
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| Challenge: | Existing algorithms to detect articles that counter the arguments in debate speeches are unsuccessful, suggesting room for further research. |
| Approach: | They propose a task to detect articles that counter the arguments made in debate speeches by annotating them from a dataset of 3,685 such speeches. |
| Outcome: | The proposed algorithm can detect articles that counter the arguments made in debate speeches, and some are successful, but none are human-like. |