FOFE is a simple technique to (almost) uniquely map any variable-length sequence into a fixed-size representation, which is particularly suitable for neural networks. It also has an appealing property that the far-away context will be gradually forgotten in the representation, which is good to model natural languages.
We have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNNLMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular RNN-LMs.