Papers by Yonatan Bilu
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion (P19-1)
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Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argumentation mining have left much of the relevant argumentative content out of reach. |
| Approach: | They propose a task of Debate Topic Expansion to find related topics for a given debate topic, along with an annotated dataset for the task. |
| Outcome: | The proposed algorithms differ from well-studied lexical-semantic relations and show they work well in argumentation mining. |
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 . |
Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
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. |
Multilingual Argument Mining: Datasets and Analysis (2020.findings-emnlp)
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| Challenge: | Argument mining tasks in non-English languages are dominated by English . we use a pre-trained language model that supports 104 languages to train models . |
| Approach: | They propose a multilingual BERT model to address argument mining tasks in non-English languages . they use English datasets and machine translation to facilitate transfer learning . |
| Outcome: | The proposed model is well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments. |
Argument Invention from First Principles (P19-1)
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Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim
| Challenge: | Argument Invention is a task that is often referred to as a natural way of inventing arguments, but has not been formalized in the context of NLP. |
| Approach: | They propose to define a taxonomy of recurring arguments and to automatically identify which of them are relevant to the topic. |
| Outcome: | The proposed taxonomy is coherent, covers the relevant topics and coincides with what debaters actually argue in their speeches, and facilitates automatic argument invention for new topics. |
The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation (2020.findings-emnlp)
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| Challenge: | Argument generation is a challenging task whose impact on social media is growing . we examine how argument generation can be enhanced to provide better arguments . |
| Approach: | They propose a pipeline for argument generation based on GPT-2 . they examine the types of claims it produces, and their veracity . |
| Outcome: | The proposed pipeline improves argument generation quality and provides a clear stance on a debate topic. |
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. |
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. |