Cross-Domain Label-Adaptive Stance Detection

Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein


Abstract
Stance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic. Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol. All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches. In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them. Moreover, we propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines, both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for unseen targets. Finally, we perform an exhaustive analysis of the cross-domain results, and we highlight the important factors influencing the model performance.
Anthology ID:
2021.emnlp-main.710
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9011–9028
Language:
URL:
https://aclanthology.org/2021.emnlp-main.710
DOI:
10.18653/v1/2021.emnlp-main.710
Bibkey:
Cite (ACL):
Momchil Hardalov, Arnav Arora, Preslav Nakov, and Isabelle Augenstein. 2021. Cross-Domain Label-Adaptive Stance Detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9011–9028, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Cross-Domain Label-Adaptive Stance Detection (Hardalov et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.710.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.710.mp4
Code
 checkstep/mole-stance +  additional community code