基于对抗性语言适应的零样本跨语言立场检测
Zero-shot Cross-lingual Stance Detection via Adversarial Language Adaptation
AI总结：
本文提出零样本跨语言立场检测方法MTAB，通过结合翻译增强与对抗学习，在缺乏目标语言训练数据时有效提升了多语言立场分类性能。
摘要：
立场检测作为一项判断社交媒体帖子对特定问题（如对疫苗的支持）持正面、负面或中立态度的任务，已被广泛研究。然而，立场检测研究通常局限于单一语言；在涉及多种语言的研究中，以往工作主要关注few-shot（少样本）设置，忽视了开发零样本跨语言立场检测模型的挑战。本文首次进行了此类尝试，提出了一种零样本跨语言立场检测的新方法——Multilingual Translation-Augmented BERT (MTAB)，旨在缺乏目标语言显式训练数据的情况下，提升跨语言分类器的性能。我们的技术采用翻译增强来改善零样本表现，并将其与对抗学习相结合以进一步提升模型效能。通过在包含英语、德语、法语和意大利语四种语言且标注了疫苗立场的多个数据集上进行实验，我们展示了所提方法的有效性，证明了与强基线模型及该模型的消融版本相比，其结果均有提升。实验证明了模型各组件的有效性，尤其是翻译增强数据以及对抗学习组件对提升模型性能的贡献。我们已在GitHub上公开了源代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
