@inproceedings{barrow-etal-2021-syntopical,
title = "Syntopical Graphs for Computational Argumentation Tasks",
author = "Barrow, Joe and
Jain, Rajiv and
Lipka, Nedim and
Dernoncourt, Franck and
Morariu, Vlad and
Manjunatha, Varun and
Oard, Douglas and
Resnik, Philip and
Wachsmuth, Henning",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.126",
doi = "10.18653/v1/2021.acl-long.126",
pages = "1583--1595",
abstract = "Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.",
}
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<abstract>Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.</abstract>
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%0 Conference Proceedings
%T Syntopical Graphs for Computational Argumentation Tasks
%A Barrow, Joe
%A Jain, Rajiv
%A Lipka, Nedim
%A Dernoncourt, Franck
%A Morariu, Vlad
%A Manjunatha, Varun
%A Oard, Douglas
%A Resnik, Philip
%A Wachsmuth, Henning
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F barrow-etal-2021-syntopical
%X Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection. We introduce a general approach to these tasks motivated by syntopical reading, a reading process that emphasizes comparing and contrasting viewpoints in order to improve topic understanding. To capture collection-level context, we introduce the syntopical graph, a data structure for linking claims within a collection. A syntopical graph is a typed multi-graph where nodes represent claims and edges represent different possible pairwise relationships, such as entailment, paraphrase, or support. Experiments applying syntopical graphs to the problems of detecting stance and aspects demonstrate state-of-the-art performance in each domain, significantly outperforming approaches that do not utilize collection-level information.
%R 10.18653/v1/2021.acl-long.126
%U https://aclanthology.org/2021.acl-long.126
%U https://doi.org/10.18653/v1/2021.acl-long.126
%P 1583-1595
Markdown (Informal)
[Syntopical Graphs for Computational Argumentation Tasks](https://aclanthology.org/2021.acl-long.126) (Barrow et al., ACL-IJCNLP 2021)
ACL
- Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, and Henning Wachsmuth. 2021. Syntopical Graphs for Computational Argumentation Tasks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1583–1595, Online. Association for Computational Linguistics.