  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.
