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.

本论文旨在将英语在反谬误领域的研究成果转移到其他语言上，提出了一种利用领域自适应技术的黑盒式非侵入式方法，通过对低质量数据集的监督与非监督学习使领域差距减小，在不需要人类专家的情况下，可在 Zulu 语言的立场检测方面达到与英语类似的结果，并提供了 Zulu 语言的数据集。实验结果表明，利用英语数据集和机器翻译技术可提高英语数据以及其他语言的性能。