  Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from
sentences, where each triplet includes an entity, its associated sentiment, and
the opinion span explaining the reason for the sentiment. Most existing
research addresses this problem in a multi-stage pipeline manner, which
neglects the mutual information between such three elements and has the problem
of error propagation. In this paper, we propose a Semantic and Syntactic
Enhanced aspect Sentiment triplet Extraction model (S3E2) to fully exploit the
syntactic and semantic relationships between the triplet elements and jointly
extract them. Specifically, we design a Graph-Sequence duel representation and
modeling paradigm for the task of ASTE: we represent the semantic and syntactic
relationships between word pairs in a sentence by graph and encode it by Graph
Neural Networks (GNNs), as well as modeling the original sentence by LSTM to
preserve the sequential information. Under this setting, we further apply a
more efficient inference strategy for the extraction of triplets. Extensive
evaluations on four benchmark datasets show that S3E2 significantly outperforms
existing approaches, which proves our S3E2's superiority and flexibility in an
end-to-end fashion.
