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


Abstract
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.
Anthology ID:
W19-4507
Volume:
Proceedings of the 6th Workshop on Argument Mining
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–66
Language:
URL:
https://aclanthology.org/W19-4507
DOI:
10.18653/v1/W19-4507
Bibkey:
Cite (ACL):
Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, and Noam Slonim. 2019. Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining. In Proceedings of the 6th Workshop on Argument Mining, pages 58–66, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining (Lavee et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4507.pdf