Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation

• URL: http://arxiv.org/abs/2404.14339v1
• Date: Mon, 22 Apr 2024 16:56:43 GMT
• Title: Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation
• Authors: Bharathi A, Arkaitz Zubiaga,
• Abstract summary: This paper introduces a novel approach to zero-shot cross-lingual stance detection, Multilingual Translation-Augmented BERT (MTAB) Our technique employs translation augmentation to improve zero-shot performance and pairs it with adversarial learning to further boost model efficacy. 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.
• Score: 7.242609314791262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We have made our source code accessible on GitHub.

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