The paper introduces the SCRAP framework for Aspect-Sentiment Quad Prediction (ASQP) using Extract-Then-Assign reasoning strategy. SCRAP aims to improve model interpretability and accuracy by generating diverse reasoning paths and selecting final predictions through consistency voting. Extensive experiments demonstrate SCRAP's superior performance over state-of-the-art models in ASQP tasks.
The ASQP task involves predicting quadruplets comprising aspect term, opinion term, aspect category, and sentiment polarity. Existing generative methods face challenges like imprecise predictions and limited interpretability due to data scarcity. The proposed SCRAP framework addresses these issues by optimizing model reasoning and prediction processes.
SCRAP leverages large language models (LLMs) to generate diverse reasoning paths via Chain-of-Thought prompting. The framework fine-tunes models for quad prediction by combining generated reasoning with ground-truth quadruplets. Self-consistent quad prediction is achieved through filtering noisy outputs based on self-consistency.
Experimental results show that SCRAP outperforms baseline methods in ASQP performance, especially with larger backbone models. Diverse reasoning paths contribute to higher accuracy, while Extract-Then-Assign reasoning enhances interpretability of quad predictions. The study acknowledges limitations related to model size and computational costs but emphasizes the efficacy of integrating reasoning into ABSA tasks.
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by Jieyong Kim,... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00354.pdfDeeper Inquiries