@inproceedings{he-etal-2022-infusing,
title = "Infusing Knowledge from {W}ikipedia to Enhance Stance Detection",
author = "He, Zihao and
Mokhberian, Negar and
Lerman, Kristina",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Barriere, Valentin and
Tafreshi, Shabnam and
Alqahtani, Sawsan and
Sedoc, Jo{\~a}o and
Klinger, Roman and
Balahur, Alexandra",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.7",
doi = "10.18653/v1/2022.wassa-1.7",
pages = "71--77",
abstract = "Stance detection infers a text author{'}s attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.",
}
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<abstract>Stance detection infers a text author’s attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.</abstract>
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%0 Conference Proceedings
%T Infusing Knowledge from Wikipedia to Enhance Stance Detection
%A He, Zihao
%A Mokhberian, Negar
%A Lerman, Kristina
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Alqahtani, Sawsan
%Y Sedoc, João
%Y Klinger, Roman
%Y Balahur, Alexandra
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F he-etal-2022-infusing
%X Stance detection infers a text author’s attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.
%R 10.18653/v1/2022.wassa-1.7
%U https://aclanthology.org/2022.wassa-1.7
%U https://doi.org/10.18653/v1/2022.wassa-1.7
%P 71-77
Markdown (Informal)
[Infusing Knowledge from Wikipedia to Enhance Stance Detection](https://aclanthology.org/2022.wassa-1.7) (He et al., WASSA 2022)
ACL
- Zihao He, Negar Mokhberian, and Kristina Lerman. 2022. Infusing Knowledge from Wikipedia to Enhance Stance Detection. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 71–77, Dublin, Ireland. Association for Computational Linguistics.