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 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 article makes the first such effort by introducing a novel approach to zero-shot cross-lingual stance detection, multilingual translation-augmented bidirectional encoder representations from Transformers (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. We have made our source code accessible on GitHub: https://github.com/amcs18pd05/MTAB-cross-lingual-vaccine-stance-detection-2.