Zero-shot cross-lingual stance detection via adversarial language adaptation

Bharathi A, Arkaitz Zubiaga

PeerJ Computer Science. 2025.

Stance detection has been widely studied as the task of determining if a social media post is positive, negative or neutral towards a specific issue, such as support towards vaccines. Research in stance detection has however often been limited to a single language and, where more than one language has been studied, research has focused on few-shot settings, overlooking the challenges of developing a zero-shot cross-lingual stance detection model. This paper makes the first such effort by introducing a novel approach to zero-shot cross-lingual stance detection, Multilingual Translation-Augmented BERT (MTAB), aiming to enhance the performance of a cross-lingual classifier in the absence of explicit training data for target languages. Our technique employs translation augmentation to improve zero-shot performance and pairs it with adversarial learning to further boost model efficacy. Through experiments on datasets labeled for stance towards vaccines in four languages --English, German, French, Italian--, we demonstrate the effectiveness of our proposed approach, showcasing improved results in comparison to a strong baseline model as well as ablated versions of our model. Our experiments demonstrate the effectiveness of model components, not least the translation-augmented data as well as the adversarial learning component, to the improved performance of the model.

@article{bharathi2025zero,
  title={Zero-shot cross-lingual stance detection via adversarial language adaptation},
  author={Bharathi, A and Zubiaga, Arkaitz},
  journal={PeerJ Computer Science},
  volume={11},
  pages={e2955},
  year={2025},
  publisher={PeerJ Inc.}
}