Exploring the Role of Argument Structure in Online Debate Persuasion

Jialu Li, Esin Durmus, Claire Cardie


Abstract
Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the best predictive performance in assessing the persuasiveness of the arguments on online debates.
Anthology ID:
2020.emnlp-main.716
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8905–8912
Language:
URL:
https://aclanthology.org/2020.emnlp-main.716
DOI:
10.18653/v1/2020.emnlp-main.716
Bibkey:
Cite (ACL):
Jialu Li, Esin Durmus, and Claire Cardie. 2020. Exploring the Role of Argument Structure in Online Debate Persuasion. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8905–8912, Online. Association for Computational Linguistics.
Cite (Informal):
Exploring the Role of Argument Structure in Online Debate Persuasion (Li et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.716.pdf
Video:
 https://slideslive.com/38938980