Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
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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.
Originalsprog | Engelsk |
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Tidsskrift | Proceedings of the International Joint Conference on Artificial Intelligence |
Vol/bind | 36 |
Udgave nummer | 10 |
Sider (fra-til) | 10729-10737. |
ISSN | 1045-0823 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 36th AAAI Conference on Artificial Intelligence (AAAI-22) - Vancouver, BC, Canada Varighed: 28 feb. 2022 → 1 mar. 2022 |
Konference
Konference | 36th AAAI Conference on Artificial Intelligence (AAAI-22) |
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Land | Canada |
By | Vancouver, BC |
Periode | 28/02/2022 → 01/03/2022 |
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