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

21 July 2019
Martin Gleize
Eyal Shnarch
Leshem Choshen
Lena Dankin
Guy Moshkowich
R. Aharonov
Noam Slonim
    AAML
ArXiv (abs)PDFHTML
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.

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