Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network

Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim


Abstract
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.
Anthology ID:
P19-1093
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
967–976
Language:
URL:
https://aclanthology.org/P19-1093
DOI:
10.18653/v1/P19-1093
Bibkey:
Cite (ACL):
Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, and Noam Slonim. 2019. Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 967–976, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (Gleize et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1093.pdf
Video:
 https://vimeo.com/384469154