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Aspect-based sentiment analysis (ABSA) task is a multi-grained task ofnatural language processing and consists of two subtasks: aspect termextraction (ATE) and aspect polarity classification (APC). Most of the existingwork focuses on the subtask of aspect term polarity inferring and ignores thesignificance of aspect term extraction. Besides, the existing researches do notpay attention to the research of the Chinese-oriented ABSA task. Based on thelocal context focus (LCF) mechanism, this paper firstly proposes a multi-tasklearning model for Chinese-oriented aspect-based sentiment analysis, namelyLCF-ATEPC. Compared with existing models, this model equips the capability ofextracting aspect term and inferring aspect term polarity synchronously,moreover, this model is effective to analyze both Chinese and English commentssimultaneously and the experiment on a multilingual mixed dataset proved itsavailability. By integrating the domain-adapted BERT model, the LCF-ATEPC modelachieved the state-of-the-art performance of aspect term extraction and aspectpolarity classification in four Chinese review datasets. Besides, theexperimental results on the most commonly used SemEval-2014 task4 Restaurantand Laptop datasets outperform the state-of-the-art performance on the ATE andAPC subtask.

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction