Memory: An adaptive contextual Memory framework for aspect-based dialogue sentiment quadruple analysis

Abstract

Aspect-based dialogue sentiment quadruple analysis (DiaASQ) is a critical task in sentiment analysis, aiming to extract sentiment quadruples (target, aspect, opinion, sentiment polarity) from dialogues. Existing methods primarily focus on single-sentence sentiment analysis, often neglecting the rich contextual information and long-range dependencies in multi-turn dialogues. To address this limitation, we propose a novel memory framework, Memory, which incorporates adaptive contextual memory mechanisms to simulate human-like emotional refinement during conversations. Our framework consists of three key components: a Contextual Knowledge Memorizer to capture token-level syntactic-semantic dependencies, an Utterance-level Sentiment Interactor to model speaker-respondent dynamics, and a Multi-granularity Memory Integrator to fuse token-level and utterance-level information for precise sentiment relationship extraction. Extensive experiments on two benchmark datasets demonstrate the framework’s superiority, achieving 10.14% and 6.03% improvements in Micro-F1, and 13.07% and 5.60% improvements in Iden-F1 on Chinese and English datasets, respectively.