Adversarial Domain Adaptation for Stance Detection

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.

Accepted at NIPS-CL-2018, Stance Detection, Fact Checking, Adversarial Domain Adaptation

References in corpus (3)

• Distributed Representations of Words and Phrases and their Compositionality
• Deep Learning for Answer Sentence Selection
• A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

Cited by in corpus (5)

• Unseen Target Stance Detection with Adversarial Domain Generalization
• University of Copenhagen Participation in TREC Health Misinformation Track 2020
• Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management
• Rating Facts under Coarse-to-fine Regimes
• Towards Explainable Fact Checking