Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks
复制标题
DOI：
10.18653/v1/2021.naacl-main.378
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Zixuan Ke;Hu Xu;Bing Liu
中科院分区：
文献类型：
--
作者：
Zixuan Ke;Hu Xu;Bing Liu
本文研究了一系列方面情感分类（ASC）任务的持续学习（CL）。虽然已经提出了一些CL技术用于文档情感分类，但我们不知道ASC上的任何CL工作。增量学习ASC任务序列的CL系统应该解决以下两个问题：（1）将从先前任务中学习的知识转移到新任务中，以帮助它学习更好的模型，以及（2）保持先前任务的模型性能，以便它们不会被遗忘。本文提出了一种新的基于胶囊网络的模型B-CL来解决这些问题。B-CL通过前向和后向知识转移显著提高了ASC在新任务和旧任务上的绩效。通过大量的实验证明了B-CL的有效性。
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.