  We introduce a new structure for memory neural networks, called feedforward
sequential memory networks (FSMN), which can learn long-term dependency without
using recurrent feedback. The proposed FSMN is a standard feedforward neural
networks equipped with learnable sequential memory blocks in the hidden layers.
In this work, we have applied FSMN to several language modeling (LM) tasks.
Experimental results have shown that the memory blocks in FSMN can learn
effective representations of long history. Experiments have shown that FSMN
based language models can significantly outperform not only feedforward neural
network (FNN) based LMs but also the popular recurrent neural network (RNN)
LMs.
