Forward and Backward Knowledge Transfer for Sentiment Classification


Hao Wang, Bing Liu, Shuai Wang, Nianzu Ma, Yan Yang ;
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:457-472, 2019.


This paper studies the problem of learning a sequence of sentiment classification tasks. The learned knowledge from each task is retained and later used to help future or subsequent task learning. This learning paradigm is called \textit{lifelong learning}. However, existing lifelong learning methods either only transfer knowledge forward to help future learning and do not go back to improve the model of a previous task or require the training data of the previous task to retrain its model to exploit backward/reverse knowledge transfer. This paper studies reverse knowledge transfer of lifelong learning. It aims to improve the model of a previous task by leveraging future knowledge without retraining using its training data, which is challenging now. In this work, this is done by exploiting a key characteristic of the generative model of naïve Bayes. That is, it is possible to improve the naïve Bayesian classifier for a task by improving its model parameters directly using the retained knowledge from other tasks. Experimental results show that the proposed method markedly outperforms existing lifelong learning baselines.

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