Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction

TL;DR

This paper introduces D2E2S, a dual-encoder model that effectively combines syntactic and semantic information for aspect sentiment triplet extraction, outperforming existing methods on benchmark datasets.

Contribution

The paper presents a novel dual-encoder architecture with a heterogeneous feature interaction module to better exploit syntactic and semantic cues in ASTE.

Findings

D2E2S surpasses state-of-the-art performance on benchmark datasets.

The dual-channel encoder effectively captures complementary syntactic and semantic features.

The heterogeneous feature interaction module improves the extraction accuracy.

Abstract

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.