A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis

4 Jan 2021  ·  Yue Mao, Yi Shen, Chao Yu, Longjun Cai ·

Aspect based sentiment analysis (ABSA) involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Early works only focused on solving one of these subtasks individually. Some recent work focused on solving a combination of two subtasks, e.g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely. More recently, the triple extraction task has been proposed, i.e., extracting the (aspect term, opinion term, sentiment polarity) triples from a sentence. However, previous approaches fail to solve all subtasks in a unified end-to-end framework. In this paper, we propose a complete solution for ABSA. We construct two machine reading comprehension (MRC) problems and solve all subtasks by joint training two BERT-MRC models with parameters sharing. We conduct experiments on these subtasks, and results on several benchmark datasets demonstrate the effectiveness of our proposed framework, which significantly outperforms existing state-of-the-art methods.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Aspect Term Extraction and Sentiment Classification SemEval Dual-MRC Avg F1 68.99 # 2
Restaurant 2014 (F1) 75.95 # 1
Laptop 2014 (F1) 65.94 # 2
Restaurant 2015 (F1) 65.08 # 3
Aspect Sentiment Triplet Extraction SemEval Dual-MRC F1 70.32 # 2
Aspect-oriented Opinion Extraction SemEval 2014 Task 4 Sub Task 2 Dual-MRC Restaurant 2014 (F1) 83.73 # 2
Laptop 2014 (F1) 79.90 # 2
Restaurant 2015 (F1) 74.50 # 3
Restaurant 2016 (F1) 83.33 # 4


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