@inproceedings{jo-etal-2018-attentive,
title = "Attentive Interaction Model: Modeling Changes in View in Argumentation",
author = "Jo, Yohan and
Poddar, Shivani and
Jeon, Byungsoo and
Shen, Qinlan and
Ros{\'e}, Carolyn and
Neubig, Graham",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1010",
doi = "10.18653/v1/N18-1010",
pages = "103--116",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Attentive Interaction Model: Modeling Changes in View in Argumentation
%A Jo, Yohan
%A Poddar, Shivani
%A Jeon, Byungsoo
%A Shen, Qinlan
%A Rosé, Carolyn
%A Neubig, Graham
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F jo-etal-2018-attentive
%X 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.
%R 10.18653/v1/N18-1010
%U https://aclanthology.org/N18-1010
%U https://doi.org/10.18653/v1/N18-1010
%P 103-116
Markdown (Informal)
[Attentive Interaction Model: Modeling Changes in View in Argumentation](https://aclanthology.org/N18-1010) (Jo et al., NAACL 2018)
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.