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

• URL: http://arxiv.org/abs/2311.10373v1
• Date: Fri, 17 Nov 2023 07:56:01 GMT
• Title: FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction
• Authors: Ting Xu, Zhen Wu, Huiyun Yang, Xinyu Dai
• Abstract summary: Aspect Sentiment Triplet Extraction (ASTE) has achieved promising results while relying on sufficient annotation data in a specific 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. To effectively transfer the knowledge across domains and extract the sentiment triplets accurately, we propose a method named Fine-grained cOntrAstive Learning.
• Score: 28.49399937940077
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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