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

Xiaowei Zhao, Yong Zhou, Xiujuan Xu

TL;DR

The paper addresses fine-grained ASTE by leveraging both syntactic and semantic information through a dual-encoder architecture (one semantic BERT channel and one syntactic-enhanced LSTM channel). It introduces the HFIM to coherently fuse SynGCN and SemGCN representations via SADPool and multi-layer graph convolutions, along with a mechanism to separate syntactic and semantic similarity using KL-divergence. Empirical results on four benchmark datasets show state-of-the-art performance, with ablations confirming the necessity of each component and a case study illustrating improved handling of long-distance relations. The approach offers a robust path to more accurate aspect-opinion-sentiment triplet extraction, with practical implications for fine-grained sentiment analysis tasks.

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.

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

TL;DR

The paper addresses fine-grained ASTE by leveraging both syntactic and semantic information through a dual-encoder architecture (one semantic BERT channel and one syntactic-enhanced LSTM channel). It introduces the HFIM to coherently fuse SynGCN and SemGCN representations via SADPool and multi-layer graph convolutions, along with a mechanism to separate syntactic and semantic similarity using KL-divergence. Empirical results on four benchmark datasets show state-of-the-art performance, with ablations confirming the necessity of each component and a case study illustrating improved handling of long-distance relations. The approach offers a robust path to more accurate aspect-opinion-sentiment triplet extraction, with practical implications for fine-grained sentiment analysis tasks.

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.
Paper Structure (31 sections, 28 equations, 3 figures, 3 tables)

This paper contains 31 sections, 28 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Task Definition
  5. D2E2S Model
  6. Input and Encoding Layer
  7. Syntactic and Semantic Graph Convolutional Networks Construction
  8. SynGCN
  9. SemGCN
  10. Syntactic and Semantic Similarity Separation
  11. Heterogeneous Features Interact Module
  12. Self-Attention Double-Pooling
  13. GCNConv
  14. GatedGraphConv
  15. Heterogeneous Features Interact
  16. ...and 16 more sections

Figures (3)

  • Figure 1: An example of the ASTE task. Aspect terms and opinion terms are highlighted in red and blue, respectively. Positive sentiment polarity is denoted by the color green, while purple symbolizes negative sentiment polarity.
  • Figure 2: The overall architecture of our D2E2S model. The purple and light green arrows represent the LSTM and BERT channels respectively.
  • Figure 3: An example of the ASTE task. Aspect terms and opinion terms are highlighted in green and orange, respectively. Positive sentiment polarity is denoted by the color red.