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Recurrent neural language models are the state-of-the-art models for languagemodeling. When the vocabulary size is large, the space taken to store the modelparameters becomes the bottleneck for the use of recurrent neural languagemodels. In this paper, we introduce a simple space compression method thatrandomly shares the structured parameters at both the input and outputembedding layers of the recurrent neural language models to significantlyreduce the size of model parameters, but still compactly represent the originalinput and output embedding layers. The method is easy to implement and tune.Experiments on several data sets show that the new method can get similarperplexity and BLEU score results while only using a very tiny fraction ofparameters.

Slim Embedding Layers for Recurrent Neural Language Models