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Inducing sparseness while training neural networks has been shown to yieldmodels with a lower memory footprint but similar effectiveness to dense models.However, sparseness is typically induced starting from a dense model, and thusthis advantage does not hold during training. We propose techniques to enforcesparseness upfront in recurrent sequence models for NLP applications, to alsobenefit training. First, in language modeling, we show how to increase hiddenstate sizes in recurrent layers without increasing the number of parameters,leading to more expressive models. Second, for sequence labeling, we show thatword embeddings with predefined sparseness lead to similar performance as denseembeddings, at a fraction of the number of trainable parameters.

Predefined Sparseness in Recurrent Sequence Models