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.

在多语言状态检测领域，本论文首次引入零样本跨语言状态检测方法（MTAB），通过翻译增强和对抗学习提升了模型效果，实验证明了该方法的有效性。