Aspect-based Sentiment Analysis as Machine Reading Comprehension
Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance.
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Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Machine Reading ComprehensionPrompt LearningReading ComprehensionSentiment AnalysisSimilar Papers 제목 키워드 기반
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