Adversarial Topic-Aware Memory Network for Cross-Lingual Stance Detection

Abstract

Stance detection is an important research area in social media analytics and text mining and has a number of security-related applications, which aims to identify the user's viewpoint towards specific targets in domains such as social security, politics and public events. Previous research on stance detection has mainly focused on monolingual scenarios with the limited number of targets, while little attention was paid to cross-lingual stance detection. In contrast to the abundant labeled data in source language (typically English), the labeled data are often scarce in the non-English target language. Meanwhile, the diverse expressions of target further complicate the task and bring additional challenges. In this paper, we focus on cross-lingual stance detection in practical applications, where target is generally expressed as a topic and no labeled data are available for the target language. To tackle the above challenges, we propose an adversarial topic-aware memory network (ATOM) for cross-lingual stance detection. Specifically, our method first mines the generalized topic representations across source and target languages and utilizes them as the guidance to transfer knowledge from the high-resource source language to the low-resource target language. We further develop an iterative memory network to facilitate knowledge transfer across languages, which adaptively generates language-invariant topic-aware clues via adversarial training. Experimental results on three multilingual datasets in the politics domain demonstrate the effectiveness of our proposed method.