Cross-Domain Label-Adaptive Stance Detection
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Dokumenter
- Cross-Domain Label-Adaptive Stance Detection
Forlagets udgivne version, 2,43 MB, PDF-dokument
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.
Originalsprog | Engelsk |
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Titel | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing |
Forlag | Association for Computational Linguistics |
Publikationsdato | 2021 |
Sider | 9011-9028 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 2021 Conference on Empirical Methods in Natural Language Processing - Online Varighed: 1 nov. 2021 → 1 nov. 2021 |
Konference
Konference | 2021 Conference on Empirical Methods in Natural Language Processing |
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By | Online |
Periode | 01/11/2021 → 01/11/2021 |
ID: 299691261