Cross-Domain Label-Adaptive Stance Detection
Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 outof-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) indomain, i.e., for seen targets, and (ii) out-ofdomain, i.e., for unseen targets.Finally, we perform an exhaustive analysis of the crossdomain results, and we highlight the important factors influencing the model performance.