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.
Abstract (translated)
作为一种确定社交媒体帖子是否支持、反对或中立的特定问题（如对疫苗的支持）的任务，姿态检测（Stance detection）已经受到了广泛研究。然而，姿态检测研究通常局限于一种语言，并且在研究多个语言时，研究重点在于少样本设置，忽略了开发零样本跨语言姿态检测模型的挑战。本文通过引入一种名为多语言翻译增强BERT（MTAB）的新方法，第一次在零样本跨语言姿态检测上做出了尝试，旨在提高在没有明确目标语言训练数据的情况下跨语言分类器的性能。我们的技术采用翻译增强来提高零样本性能，并将其与对抗学习相结合，以进一步提高模型的功效。通过在四个语言（英语、德语、法语、意大利）的数据集上进行实验，我们证明了所提出方法的效力，并将其与强基线模型以及我们模型的衰减版本进行了比较。我们的实验结果表明，模型组件（尤其是翻译增强数据和对抗学习组件）对模型的提高性能具有重要作用。我们在GitHub上公开了我们的源代码。
URL
https://arxiv.org/abs/2404.14339
