  This paper studies the problem of learning a sequence of sentiment
classification tasks. The learned knowledge from each task is retained and used
to help future or subsequent task learning. This learning paradigm is called
Lifelong Learning (LL). However, existing LL 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 LL in the context of naive Bayesian (NB)
classification. It aims to improve the model of a previous task by leveraging
future knowledge without retraining using its training data. This is done by
exploiting a key characteristic of the generative model of NB. That is, it is
possible to improve the NB classifier for a task by improving its model
parameters directly by using the retained knowledge from other tasks.
Experimental results show that the proposed method markedly outperforms
existing LL baselines.
