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

arXiv: https://arxiv.org/abs/2402.15370 · HTML: https://mlsanity.com/paper/2402.15370/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [A Unified Generative Framework for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2106.04300/) (2021)
- [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2010.02609/) (2020)
- [A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis](https://mlsanity.com/paper/2101.00816/) (2021)
- [Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2107.12214/) (2021)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2103.07665/) (2021)
- [Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extractionwith Rich Syntactic Knowledge](https://mlsanity.com/paper/2105.02520/) (2021)
- [Structure-Feature based Graph Self-adaptive Pooling](https://mlsanity.com/paper/2002.00848/) (2020)
- [Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2106.03315/) (2021)
- [Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning](https://mlsanity.com/paper/2010.13378/) (2020)
- [A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2204.12674/) (2022)
- [An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction](https://mlsanity.com/paper/2109.01238/) (2021)
- [CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2310.15577/) (2023)

## Built on by (most cited first, 2 of 2)

- [T-T: Table Transformer for Tagging-based Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2505.05271/) (2025)
- [TCDA: Thread-Constrained Discourse-Aware Modeling for Conversational Sentiment Quadruple Analysis](https://mlsanity.com/paper/2605.01717/) (2026)

## Abstract (arXiv)

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 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.