Dialogues Aspect-based Sentiment Quadruple Extraction via Structural Entropy Minimization Partitioning

TL;DR

This paper introduces a novel method for extracting aspect-based sentiment quadruples from dialogues by partitioning dialogues into semantically independent sub-dialogues using structural entropy minimization, improving accuracy and efficiency.

Contribution

The paper proposes a structural entropy minimization algorithm for dialogue partitioning and a two-step extraction framework, advancing the state-of-the-art in dialogue-based sentiment quadruple extraction.

Findings

Achieves state-of-the-art performance on DiaASQ tasks.

Reduces computational costs significantly.

Effectively isolates semantically independent sub-dialogues.

Abstract

Dialogues Aspect-based Sentiment Quadruple Extraction (DiaASQ) aims to extract all target-aspect-opinion-sentiment quadruples from a given multi-round, multi-participant dialogue. Existing methods typically learn word relations across entire dialogues, assuming a uniform distribution of sentiment elements. However, we find that dialogues often contain multiple semantically independent sub-dialogues without clear dependencies between them. Therefore, learning word relationships across the entire dialogue inevitably introduces additional noise into the extraction process. To address this, our method focuses on partitioning dialogues into semantically independent sub-dialogues. Achieving completeness while minimizing these sub-dialogues presents a significant challenge. Simply partitioning based on reply relationships is ineffective. Instead, we propose utilizing a structural entropy minimization algorithm to partition the dialogues. This approach aims to preserve relevant utterances while distinguishing irrelevant ones as much as possible. Furthermore, we introduce a two-step framework for quadruple extraction: first extracting individual sentiment elements at the utterance level, then matching quadruples at the sub-dialogue level. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in DiaASQ with much lower computational costs.