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Aspect Sentiment Triplet Extraction (ASTE) has achieved promising resultswhile relying on sufficient annotation data in a specific domain. However, itis infeasible to annotate data for each individual domain. We propose toexplore ASTE in the cross-domain setting, which transfers knowledge from aresource-rich source domain to a resource-poor target domain, therebyalleviating the reliance on labeled data in the target domain. To effectivelytransfer the knowledge across domains and extract the sentiment tripletsaccurately, we propose a method named Fine-grained cOntrAstive Learning (FOAL)to reduce the domain discrepancy and preserve the discriminability of eachcategory. Experiments on six transfer pairs show that FOAL achieves 6%performance gains and reduces the domain discrepancy significantly comparedwith strong baselines. Our code will be publicly available once accepted.

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