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

TL;DR

This paper introduces a sentiment-based pre-training approach for cross-lingual stance detection, demonstrating significant improvements in low-resource settings across diverse languages and datasets.

Contribution

It proposes a novel sentiment-based data generation method and a label encoder to enhance cross-lingual stance detection with limited labeled data.

Findings

Sentiment-based pre-training improves F1 scores by over 6% in low-resource settings.

The approach is effective across 12 languages and 15 datasets.

The proposed method outperforms several strong baselines.

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.