A Unified One-Step Solution for Aspect Sentiment Quad Prediction

• URL: http://arxiv.org/abs/2306.04152v1
• Date: Wed, 7 Jun 2023 05:00:01 GMT
• Title: A Unified One-Step Solution for Aspect Sentiment Quad Prediction
• Authors: Junxian Zhou, Haiqin Yang, Yuxuan He, Hao Mou, Junbo Yang
• Abstract summary: Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspect-based sentiment analysis. We release two new datasets for ASQP, which contain the following characteristics: larger size, more words per sample, and higher density. We propose a unified one-step solution for ASQP, namely One-ASQP, to detect the aspect categories and to identify the aspect-opinion-sentiment triplets simultaneously.
• Score: 3.428123050377681
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Aspect sentiment quad prediction (ASQP) is a challenging yet significant subtask in aspect-based sentiment analysis as it provides a complete aspect-level sentiment structure. However, existing ASQP datasets are usually small and low-density, hindering technical advancement. To expand the capacity, in this paper, we release two new datasets for ASQP, which contain the following characteristics: larger size, more words per sample, and higher density. With such datasets, we unveil the shortcomings of existing strong ASQP baselines and therefore propose a unified one-step solution for ASQP, namely One-ASQP, to detect the aspect categories and to identify the aspect-opinion-sentiment (AOS) triplets simultaneously. Our One-ASQP holds several unique advantages: (1) by separating ASQP into two subtasks and solving them independently and simultaneously, we can avoid error propagation in pipeline-based methods and overcome slow training and inference in generation-based methods; (2) by introducing sentiment-specific horns tagging schema in a token-pair-based two-dimensional matrix, we can exploit deeper interactions between sentiment elements and efficiently decode the AOS triplets; (3) we design ``[NULL]'' token can help us effectively identify the implicit aspects or opinions. Experiments on two benchmark datasets and our released two datasets demonstrate the advantages of our One-ASQP. The two new datasets are publicly released at \url{https://www.github.com/Datastory-CN/ASQP-Datasets}.

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