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.

本研究提出新的模型，在跨语言环境下实现了最全面的立场检测实验，通过基于情感的立场数据预训练，相较于几个强基准测试，以及低资源设置下取得了 6% 以上的 F1 绝对改善。