@inproceedings{korner-etal-2021-classifying,
title = "On Classifying whether Two Texts are on the Same Side of an Argument",
author = {K{\"o}rner, Erik and
Wiedemann, Gregor and
Hakimi, Ahmad Dawar and
Heyer, Gerhard and
Potthast, Martin},
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.795",
doi = "10.18653/v1/2021.emnlp-main.795",
pages = "10130--10138",
abstract = "To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task{'}s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.",
}
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<abstract>To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task’s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.</abstract>
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%0 Conference Proceedings
%T On Classifying whether Two Texts are on the Same Side of an Argument
%A Körner, Erik
%A Wiedemann, Gregor
%A Hakimi, Ahmad Dawar
%A Heyer, Gerhard
%A Potthast, Martin
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F korner-etal-2021-classifying
%X To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task’s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.
%R 10.18653/v1/2021.emnlp-main.795
%U https://aclanthology.org/2021.emnlp-main.795
%U https://doi.org/10.18653/v1/2021.emnlp-main.795
%P 10130-10138
Markdown (Informal)
[On Classifying whether Two Texts are on the Same Side of an Argument](https://aclanthology.org/2021.emnlp-main.795) (Körner et al., EMNLP 2021)
ACL
- Erik Körner, Gregor Wiedemann, Ahmad Dawar Hakimi, Gerhard Heyer, and Martin Potthast. 2021. On Classifying whether Two Texts are on the Same Side of an Argument. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10130–10138, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.