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This paper proposes a novel framework for recurrent neural networks (RNNs)inspired by the human memory models in the field of cognitive neuroscience toenhance information processing and transmission between adjacent RNNs' units.The proposed framework for RNNs consists of three stages that is workingmemory, forget, and long-term store. The first stage includes taking input datainto sensory memory and transferring it to working memory for preliminarytreatment. And the second stage mainly focuses on proactively forgetting thesecondary information rather than the primary in the working memory. Andfinally, we get the long-term store normally using some kind of RNN's unit. Ourframework, which is generalized and simple, is evaluated on 6 datasets whichfall into 3 different tasks, corresponding to text classification, imageclassification and language modelling. Experiments reveal that our frameworkcan obviously improve the performance of traditional recurrent neural networks.And exploratory task shows the ability of our framework of correctly forgettingthe secondary information.

A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units