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    <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.</abstract>
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%0 Conference Proceedings
%T Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
%A Gleize, Martin
%A Shnarch, Eyal
%A Choshen, Leshem
%A Dankin, Lena
%A Moshkowich, Guy
%A Aharonov, Ranit
%A Slonim, Noam
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F gleize-etal-2019-convinced
%X 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.
%R 10.18653/v1/P19-1093
%U https://aclanthology.org/P19-1093
%U https://doi.org/10.18653/v1/P19-1093
%P 967-976

Markdown (Informal)

[Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network](https://aclanthology.org/P19-1093) (Gleize et al., ACL 2019)

• Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (Gleize et al., ACL 2019)

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.