Aspect Sentiment Quad Prediction as Paraphrase Generation

TL;DR

This paper introduces a new task called Aspect Sentiment Quad Prediction (ASQP) that jointly detects all sentiment elements in a sentence using a paraphrase generation approach, improving accuracy and capturing complete sentiment structures.

Contribution

The paper proposes a novel paraphrase modeling paradigm for ASQP, enabling end-to-end detection of all sentiment elements and leveraging natural language semantics.

Findings

The proposed method outperforms existing approaches on benchmark datasets.

The paraphrase generation framework effectively captures complete sentiment quads.

Cross-task transfer demonstrates the model's versatility.

Abstract

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel \textsc{Paraphrase} modeling paradigm to cast the ASQP task to a paraphrase generation process. On one hand, the generation formulation allows solving ASQP in an end-to-end manner, alleviating the potential error propagation in the pipeline solution. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form. Extensive experiments on benchmark datasets show the superiority of our proposed method and the capacity of cross-task transfer with the proposed unified \textsc{Paraphrase} modeling framework.