Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2008.05179v2
• Date: Mon, 14 Mar 2022 09:36:35 GMT
• Title: Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis
• Authors: Yunlong Liang, Fandong Meng, Jinchao Zhang, Yufeng Chen, Jinan Xu, and Jie Zhou
• Abstract summary: We propose a novel non-temporal mechanism to enhance the ABSA task through modeling inter-aspect dependencies. We focus on the well-known class imbalance issue on the ABSA task and address it by down-weighting the loss assigned to well-classified instances.
• Score: 70.22725610210811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence. Although multiple aspects of a sentence appear in a non-adjacent sequential order, they are not in a strict temporal relationship as natural language sequence, thus the aspect-aware sentence representations should not be treated as temporal dependency processing. In this paper, we propose a novel non-temporal mechanism to enhance the ABSA task through modeling inter-aspect dependencies. Furthermore, we focus on the well-known class imbalance issue on the ABSA task and address it by down-weighting the loss assigned to well-classified instances. Experiments on two distinct domains of SemEval 2014 task 4 demonstrate the effectiveness of our proposed approach.

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