A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis
This work provides a complete solution for researchers and practitioners working on Aspect Based Sentiment Analysis by unifying all subtasks into a single end-to-end framework, representing an incremental improvement over previous partial solutions.
This paper proposes a unified end-to-end framework for Aspect Based Sentiment Analysis (ABSA) by constructing two machine reading comprehension (MRC) problems. The framework jointly trains two BERT-MRC models with shared parameters to solve aspect term extraction, opinion term extraction, and aspect-level sentiment classification, achieving state-of-the-art performance on several benchmark datasets.
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.