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arXiv · 1708.00214

Natural Language Processing with Small Feed-Forward Networks

Jan A. Botha·Emily Pitler·Ji Ma·Anton Bakalov·Alex Salcianu·David Weiss·Ryan McDonald·Slav Petrov
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Abstract

We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory budget.

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Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan McDonald, Slav Petrov. 2017-08-01. Natural Language Processing with Small Feed-Forward Networks. https://arxiv.org/abs/1708.00214

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