Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks

• URL: http://arxiv.org/abs/2112.03271v1
• Date: Mon, 6 Dec 2021 02:46:06 GMT
• Title: Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
• Authors: Zixuan Ke, Hu Xu, Bing Liu
• Abstract summary: This paper studies continual learning of a sequence of aspect sentiment classification (ASC) tasks. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues. A novel capsule network based model called B-CL is proposed to address these issues.
• Score: 22.28374603976649
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper studies continual learning (CL) of a sequence of aspect sentiment classification (ASC) tasks. Although some CL techniques have been proposed for document sentiment classification, we are not aware of any CL work on ASC. A CL system that incrementally learns a sequence of ASC tasks should address the following two issues: (1) transfer knowledge learned from previous tasks to the new task to help it learn a better model, and (2) maintain the performance of the models for previous tasks so that they are not forgotten. This paper proposes a novel capsule network based model called B-CL to address these issues. B-CL markedly improves the ASC performance on both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of B-CL is demonstrated through extensive experiments.

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