@inproceedings{ajjour-etal-2019-modeling,
title = "Modeling Frames in Argumentation",
author = "Ajjour, Yamen and
Alshomary, Milad and
Wachsmuth, Henning and
Stein, Benno",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1290",
doi = "10.18653/v1/D19-1290",
pages = "2922--2932",
abstract = "In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the {``}right{''} frame(s) to convince the audience to adopt the author{'}s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience{'}s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates{'} topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.",
}
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<abstract>In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author’s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience’s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates’ topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.</abstract>
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%0 Conference Proceedings
%T Modeling Frames in Argumentation
%A Ajjour, Yamen
%A Alshomary, Milad
%A Wachsmuth, Henning
%A Stein, Benno
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ajjour-etal-2019-modeling
%X In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the “right” frame(s) to convince the audience to adopt the author’s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience’s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates’ topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.
%R 10.18653/v1/D19-1290
%U https://aclanthology.org/D19-1290
%U https://doi.org/10.18653/v1/D19-1290
%P 2922-2932
Markdown (Informal)
[Modeling Frames in Argumentation](https://aclanthology.org/D19-1290) (Ajjour et al., EMNLP-IJCNLP 2019)
ACL
- Yamen Ajjour, Milad Alshomary, Henning Wachsmuth, and Benno Stein. 2019. Modeling Frames in Argumentation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2922–2932, Hong Kong, China. Association for Computational Linguistics.
