Misinformation has become a major concern in recent last years given its spread across our information sources. In the past years, many NLP tasks have been introduced in this area, with some systems reaching good results on English language datasets. Existing AI based approaches for fighting misinformation in literature suggest automatic stance detection as an integral first step to success. Our paper aims at utilizing this progress made for English to transfers that knowledge into other languages, which is a non-trivial task due to the domain gap between English and the target languages. We propose a black-box non-intrusive method that utilizes techniques from Domain Adaptation to reduce the domain gap, without requiring any human expertise in the target language, by leveraging low-quality data in both a supervised and unsupervised manner. This allows us to rapidly achieve similar results for stance detection for the Zulu language, the target language in this work, as are found for English. We also provide a stance detection dataset in the Zulu language. Our experimental results show that by leveraging English datasets and machine translation we can increase performances on both English data along with other languages.

翻译：最近几年来,由于信息散布于我们的信息来源中,错误信息已成为近年来的一个主要关注问题。在过去的几年中,许多国家语言方案的任务被引入这一领域,有些系统在英语数据集方面取得了良好结果。现有的基于AI的文献中打击错误信息的方法表明,自动发现姿态是成功的第一步。我们的文件旨在利用这种进展,使英语将知识传输到其他语言,由于英语与目标语言之间的域隔,这是一项非边际任务。我们提出了一种黑盒非侵入性方法,利用Domain Adit的技术来缩小域间差距,而不需要任何人类在目标语言方面的专门知识,同时以监督和不受监督的方式利用低质量的数据。这使我们能够迅速取得类似的结果,以观察工作的目标语言Zulu语(如英语)。我们还提供了Zulu语的定位检测数据集。我们的实验结果表明,通过利用英语数据集和机器翻译,我们可以与其他语言一起提高两种英语数据的性能。