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This paper studies the problem of learning a sequence of sentimentclassification tasks. The learned knowledge from each task is retained and usedto help future or subsequent task learning. This learning paradigm is calledLifelong Learning (LL). However, existing LL methods either only transferknowledge forward to help future learning and do not go back to improve themodel of a previous task or require the training data of the previous task toretrain its model to exploit backward/reverse knowledge transfer. This paperstudies reverse knowledge transfer of LL in the context of naive Bayesian (NB)classification. It aims to improve the model of a previous task by leveragingfuture knowledge without retraining using its training data. This is done byexploiting a key characteristic of the generative model of NB. That is, it ispossible to improve the NB classifier for a task by improving its modelparameters directly by using the retained knowledge from other tasks.Experimental results show that the proposed method markedly outperformsexisting LL baselines.

Forward and Backward Knowledge Transfer for Sentiment Classification