Cross-Target Stance Classification with Self-Attention Networks

2018/05/17 by Chang Xu, Cécile Paris, Xu, Chang +5 · 3 citations
Computer Science · #Domain Adaptation and Few-Shot Learning #Anomaly Detection Techniques and Applications #Human Pose and Action Recognition

paper · pdf · doi:10.48550/arxiv.1805.06593

Abstract

In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.

Cited by

• MSME: A Multi-Stage Multi-Expert Framework for Zero-Shot Stance Detection
• Multilingual Target-Stance Extraction
• MLSD: A Novel Few-Shot Learning Approach to Enhance Cross-Target and Cross-Domain Stance Detection

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