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In this paper, we propose the new fixed-size ordinally-forgetting encoding(FOFE) method, which can almost uniquely encode any variable-length sequence ofwords into a fixed-size representation. FOFE can model the word order in asequence using a simple ordinally-forgetting mechanism according to thepositions of words. In this work, we have applied FOFE to feedforward neuralnetwork language models (FNN-LMs). Experimental results have shown that withoutusing any recurrent feedbacks, FOFE based FNN-LMs can significantly outperformnot only the standard fixed-input FNN-LMs but also the popular RNN-LMs.

A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models