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

• URL: http://arxiv.org/abs/2109.06050v1
• Date: Mon, 13 Sep 2021 15:20:06 GMT
• Title: Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
• Authors: Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
• Abstract summary: We present the most comprehensive study of cross-lingual stance detection to date. We use 15 diverse datasets in 12 languages from 6 language families. For our experiments, we build on pattern-exploiting training, proposing the addition of a novel label encoder.
• Score: 32.800766653254634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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