The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with limited numbers of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training, proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in low-shot settings compared to several strong baselines.
翻译:定位探测的目的是要确定某一文本中针对某一目标表达的观点,这些观点或背景往往根据用户和平台的不同语言以许多不同语言表达,这些观点或背景取决于用户和平台,它们可以是本地新闻发布站、社交媒体平台、新闻论坛等。然而,大多数定位探测研究都局限于单一语言和少数有限目标,很少开展跨语言定位检测工作。此外,非英语来源的贴标签数据往往稀少,并带来更多的挑战。最近,大型多语言模型大大改善了许多非英语任务的业绩,特别是这类非英语任务的数量有限。这突出表明了培训前模式的重要性及其从几个实例中学习的能力。在本文件中,我们介绍了迄今为止对跨语言定位探测的最全面研究:我们试验了来自6个语言家庭12种语言的15种不同数据集,各有6个低资源评估环境。我们进行实验时,利用模式开发培训,提议增加一个新的标签来简化语言表达程序。我们进一步提议在比绝对标准更强的F1级环境中生成基于情绪的低位基准数据。