Papers by Guy Moshkowich
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. |
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. |
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (P19-1)
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Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argument detection have made it easier to identify the more convincing arguments. |
| Approach: | They propose a new data set of pairs of evidence labeled for convincingness that is more challenging than existing alternatives. |
| Outcome: | The proposed method outperforms baselines on convincingness data and its own. |
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains (2020.findings-emnlp)
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| Challenge: | Understanding unexplored data is a slow process, and there is no labeled data at hand. |
| Approach: | They propose to use unsupervised methods to reveal rules which cluster unexplored corpus by its prominent categories to help domain experts understand their texts. |
| Outcome: | The proposed rules can be bootstrapped to identify target categories and deepen understanding of the data. |