Attentive Interaction Model: Modeling Changes in View in Argumentation

Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn Rosé, Graham Neubig


Abstract
We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if the argument successfully changes the OH’s view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH’s reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH’s reasoning and that of the challenger’s argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH’s change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.
Anthology ID:
N18-1010
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–116
Language:
URL:
https://aclanthology.org/N18-1010
DOI:
10.18653/v1/N18-1010
Bibkey:
Cite (ACL):
Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn Rosé, and Graham Neubig. 2018. Attentive Interaction Model: Modeling Changes in View in Argumentation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 103–116, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Attentive Interaction Model: Modeling Changes in View in Argumentation (Jo et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1010.pdf
Code
 yohanjo/aim