Modeling Transferable Topics for Cross-Target Stance Detection

Abstract

Targeted stance detection aims to classify the attitude of an opinionated text towards a pre-defined target. Previous methods mainly focus on in-target setting that models are trained and tested using data specific to the same target. In practical cases, the target we concern may have few or no labeled data, which restrains us from training a target-specific model. In this paper we study the problem of cross-target stance detection, utilizing labeled data of a source target to learn models that can be adapted to a destination target. To this end, we propose an effective method, the core intuition of which is to leverage shared latent topics between two targets as transferable knowledge to facilitate model adaptation. Our method acquires topic knowledge with neural variational inference, and further adopts adversarial training that encourages the model to learn target-invariant representations. Experimental results verify that our proposed method is superior to the state-of-the-art methods.