Bilingual Zero-Shot Stance Detection

Zero-shot stance detection (ZSSD) aims to determine whether the author of a text is in support, against, or neutral toward a target that is unseen during training.In this paper, we investigate ZSSD within a bilingual framework and compare it with cross-lingual and monolingual scenarios, in settings that have not previously been explored.Our study focuses on both noun-phrase and claim targets within indomain and out-of-domain bilingual ZSSD scenarios.To support this research, we assemble Bi-STANCE, a comprehensive bilingual ZSSD dataset consisting of over 100,000 annotated text-target pairs in both Chinese and English, sourced from existing datasets.Additionally, we examine a more challenging aspect of bilingual ZSSD by focusing on claim targets with a low occurrence of shared words with their corresponding texts.As part of Bi-STANCE, we created an extended dataset that emphasizes this challenging scenario.To the best of our knowledge, we are the first to explore this difficult ZSSD setting.We investigate these tasks using state-of-the-art pre-trained language models (PLMs) and large language models (LLMs).We release our dataset and code at https://github.com/chenyez/BiSTANCE.