Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis

• URL: http://arxiv.org/abs/2309.15476v1
• Date: Wed, 27 Sep 2023 08:17:28 GMT
• Title: Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis
• Authors: Yuqing Li, Wenyuan Zhang, Binbin Li, Siyu Jia, Zisen Qi, Xingbang Tan
• Abstract summary: DiaASQ aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue. Existing work independently encodes each utterance, thereby struggling to capture long-range conversational context. We propose a novel Dynamic Multi-scale Context Aggregation network (DMCA) to address the challenges.
• Score: 4.768182075837568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversational aspect-based sentiment quadruple analysis (DiaASQ) aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue. In DiaASQ, a quadruple's elements often cross multiple utterances. This situation complicates the extraction process, emphasizing the need for an adequate understanding of conversational context and interactions. However, existing work independently encodes each utterance, thereby struggling to capture long-range conversational context and overlooking the deep inter-utterance dependencies. In this work, we propose a novel Dynamic Multi-scale Context Aggregation network (DMCA) to address the challenges. Specifically, we first utilize dialogue structure to generate multi-scale utterance windows for capturing rich contextual information. After that, we design a Dynamic Hierarchical Aggregation module (DHA) to integrate progressive cues between them. In addition, we form a multi-stage loss strategy to improve model performance and generalization ability. Extensive experimental results show that the DMCA model outperforms baselines significantly and achieves state-of-the-art performance.

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