Guoxin Yu
2021
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis
Guoxin Yu
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Xiang Ao
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Ling Luo
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Min Yang
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Xiaofei Sun
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Jiwei Li
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Qing He
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension
Guoxin Yu
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Jiwei Li
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Ling Luo
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Yuxian Meng
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Xiang Ao
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Qing He
Findings of the Association for Computational Linguistics: EMNLP 2021
The pivot for the unified Aspect-based Sentiment Analysis (ABSA) is to couple aspect terms with their corresponding opinion terms, which might further derive easier sentiment predictions. In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably. We propose a new paradigm named Role Flipped Machine Reading Comprehension (RF-MRC) to resolve. At its heart, the predicted results of either the Aspect Term Extraction (ATE) or the Opinion Terms Extraction (OTE) are regarded as the queries, respectively, and the matched opinion or aspect terms are considered as answers. The queries and answers can be flipped for multi-hop detection. Finally, every matched aspect-opinion pair is predicted by the sentiment classifier. RF-MRC can solve the ABSA task without any additional data annotation or transformation. Experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.
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