FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction

TL;DR

This paper introduces FOAL, a novel fine-grained contrastive learning method that enhances cross-domain aspect sentiment triplet extraction by reducing domain discrepancy and improving transferability, especially in resource-scarce target domains.

Contribution

The paper proposes FOAL, a new contrastive learning approach specifically designed for cross-domain ASTE, addressing domain adaptation challenges with significant performance improvements.

Findings

Achieves 6% performance gain over baselines.

Effectively reduces domain discrepancy.

Demonstrates strong transferability across six domain pairs.

Abstract

Aspect Sentiment Triplet Extraction (ASTE) has achieved promising results while relying on sufficient annotation data in a specific domain. However, it is infeasible to annotate data for each individual domain. We propose to explore ASTE in the cross-domain setting, which transfers knowledge from a resource-rich source domain to a resource-poor target domain, thereby alleviating the reliance on labeled data in the target domain. To effectively transfer the knowledge across domains and extract the sentiment triplets accurately, we propose a method named Fine-grained cOntrAstive Learning (FOAL) to reduce the domain discrepancy and preserve the discriminability of each category. Experiments on six transfer pairs show that FOAL achieves 6% performance gains and reduces the domain discrepancy significantly compared with strong baselines. Our code will be publicly available once accepted.