Adversarial Domain Adaptation for Stance Detection

TL;DR

This paper introduces an adversarial domain adaptation approach for stance detection, enabling models trained on one domain to effectively transfer knowledge to new domains with limited labeled data, thereby improving automated fact checking.

Contribution

It proposes a novel adversarial domain adaptation method specifically designed for stance detection, addressing the challenge of limited labeled data in target domains.

Findings

Effective transfer of stance detection knowledge across domains.

Significant improvement over baseline models in cross-domain settings.

Validated on publicly available datasets.

Abstract

This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on machine learning are viable alternatives. In this paper, we focus on adversarial domain adaptation for stance detection where we assume there exists sufficient labeled data in the source domain and limited labeled data in the target domain. Extensive experiments on publicly available datasets show the effectiveness of our domain adaption model in transferring knowledge for accurate stance detection across domains.