Two-Stage Aspect Sentiment Quadruple Prediction Based on MRC and Text Generation
Zhijun Lil, Zhenyu Yang, Xiaoyang Li, Yiwen Li · 2023
In recent years, aspect sentiment quadruple prediction (ASQP) has become popular in aspect-based sentiment analysis (ABSA). Its purpose is to decode a given sentence into aspect sentiment quadruples (aspect category, aspect term, opinion term, and sentiment polarity). When trying to efficiently extract aspect sentiment quadruples, the following problems are often encountered: Firstly, the intrinsic relationships between aspect terms and opinion terms are usually ignored, thus failing to address the correlation between establishing aspect-opinion pairs and ignoring the mutual interference between different sentiment quadruples; Secondly, the semantic information contained in the sentiment elements of comment utterances is often underutilized, thus increasing the risk of obtaining inaccurate predictions. We propose a two-stage framework to address these issues by enhancing the correlations between aspects and opinions and fully utilizing the semantic information of sentiment elements. Specifically, in the first stage, we treat the extraction task as a machine reading comprehension (MRC) problem, employ a span-based labeling scheme, and construct a question-and-answer-based MRC task to efficiently extract aspect-opinion pairs. In the second stage, we view the classification of aspect categories and sentiment polarities as a text generation task, where the semantics of sentiment elements can be leveraged by learning to generate them in natural language form. Finally, the two stages are combined with our proposed template generator, and the aspect sentiment quadruples can be decoded. We conducted experiments on two datasets, and the experimental results were superior to those of the comparison approaches, with our model demonstrating excellent performance in terms of processing complex sentences containing multiple quaternary groups. Additionally, in sub-task experiments, our model achieved good results, further proving its effectiveness. We will upload the specific code to https://github.com/SlienceLZJ/ASQP.