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.
@inproceedings{DBLP:conf/acl/GleizeSCDMAS19,
author = {Martin Gleize and
Eyal Shnarch and
Leshem Choshen and
Lena Dankin and
Guy Moshkowich and
Ranit Aharonov and
Noam Slonim},
editor = {Anna Korhonen and
David R. Traum and
Llu{\'{\i}}s M{\`{a}}rquez},
title = {Are You Convinced? Choosing the More Convincing Evidence with a Siamese
Network},
booktitle = {Proceedings of the 57th Conference of the Association for Computational
Linguistics, {ACL} 2019, Florence, Italy, July 28- August 2, 2019,
Volume 1: Long Papers},
pages = {967--976},
publisher = {Association for Computational Linguistics},
year = {2019},
url = {https://doi.org/10.18653/v1/p19-1093},
doi = {10.18653/V1/P19-1093},
timestamp = {Fri, 06 Aug 2021 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/acl/GleizeSCDMAS19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
See the full reference list in the paper.