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Communication Dans Un Congrès Année : 2022

Domain Adaptation for Stance Detection towards Unseen Target on Social Media

Ruofan Deng
  • Fonction : Auteur
Li Panl
  • Fonction : Auteur

Résumé

Stance detection aims at identifying people's stand-point towards a given target. New targets are constantly appearing on social media, making previous annotated data unusable by stance detection models relying on classical supervised machine learning. Thus, cross-target stance detection which uses labeled data from source targets to learn a model that can be adapted to the destination new target, has become a prevailing research direction. However, previous methods rely on manually chosen similar source-destination target pairs and lack generalization to unseen targets with no explicit relation to known ones. To this end, we investigate the problem from a domain adaptation perspective and further propose a novel Unified Target-aware Domain Adaptation method (UTDA) that leverages knowledge transfer capability of transformer-based language model. The proposed method can effectively extract critical target-shared features for detecting stance by feature disentanglement and automatically learn to identify target relations. UTDA can easily be applied to a new unseen target since it does not rely on any pre-defined target pairs. Experimental results on two benchmark stance datasets demonstrate that our method achieves better performance than strong baselines
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Dates et versions

hal-04275993 , version 1 (08-11-2023)

Identifiants

Citer

Ruofan Deng, Li Panl, Chloé Clavel. Domain Adaptation for Stance Detection towards Unseen Target on Social Media. 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII), Oct 2022, Nara, Japan. pp.1-8, ⟨10.1109/ACII55700.2022.9953818⟩. ⟨hal-04275993⟩
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