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

Understand

In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation.

• FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words.
• In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs).
• Experimental results have shown that without using any recurrent feedbacks, FOFE based FNN-LMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular RNN-LMs.