Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training

Research output: Contribution to journalConference articleResearchpeer-review


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.
Original languageEnglish
JournalProceedings of the International Joint Conference on Artificial Intelligence
Issue number10
Pages (from-to)10729-10737.
Publication statusPublished - 2022
Event36th AAAI Conference on Artificial Intelligence (AAAI-22) - Vancouver, BC, Canada
Duration: 28 Feb 20221 Mar 2022


Conference36th AAAI Conference on Artificial Intelligence (AAAI-22)
CityVancouver, BC

ID: 339340380