Dual Encoder: Exploiting the Potential of Syntactic and Semantic for
  Aspect Sentiment Triplet Extraction
Xiaowei Zhao Yong Zhou Xiujuan Xu
Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained
sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to
model the syntax-semantic relationships inherent in triplet elements. However,
they have yet to fully tap into the vast potential of syntactic and semantic
information within the ASTE task. In this work, we propose a \emph{Dual
Encoder: Exploiting the potential of Syntactic and Semantic} model (D2E2S),
which maximizes the syntactic and semantic relationships among words.
Specifically, our model utilizes a dual-channel encoder with a BERT channel to
capture semantic information, and an enhanced LSTM channel for comprehensive
syntactic information capture. Subsequently, we introduce the heterogeneous
feature interaction module to capture intricate interactions between dependency
syntax and attention semantics, and to dynamically select vital nodes. We
leverage the synergy of these modules to harness the significant potential of
syntactic and semantic information in ASTE tasks. Testing on public benchmarks,
our D2E2S model surpasses the current state-of-the-art(SOTA), demonstrating its
effectiveness.