Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
July 21, 2019 ยท Declared Dead ยท ๐ Annual Meeting of the Association for Computational Linguistics
"No code URL or promise found in abstract"
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Authors
Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
arXiv ID
1907.08971
Category
cs.LG: Machine Learning
Cross-listed
cs.CL,
stat.ML
Citations
73
Venue
Annual Meeting of the Association for Computational Linguistics
Last Checked
5 months ago
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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