@inproceedings{allaway-etal-2021-adversarial,
title = "Adversarial Learning for Zero-Shot Stance Detection on Social Media",
author = "Allaway, Emily and
Srikanth, Malavika and
McKeown, Kathleen",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.379",
doi = "10.18653/v1/2021.naacl-main.379",
pages = "4756--4767",
abstract = "Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.",
}
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%0 Conference Proceedings
%T Adversarial Learning for Zero-Shot Stance Detection on Social Media
%A Allaway, Emily
%A Srikanth, Malavika
%A McKeown, Kathleen
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F allaway-etal-2021-adversarial
%X Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to topics not previously considered, highlighting future directions for zero-shot transfer.
%R 10.18653/v1/2021.naacl-main.379
%U https://aclanthology.org/2021.naacl-main.379
%U https://doi.org/10.18653/v1/2021.naacl-main.379
%P 4756-4767
Markdown (Informal)
[Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://aclanthology.org/2021.naacl-main.379) (Allaway et al., NAACL 2021)
ACL
- Emily Allaway, Malavika Srikanth, and Kathleen McKeown. 2021. Adversarial Learning for Zero-Shot Stance Detection on Social Media. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4756–4767, Online. Association for Computational Linguistics.