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Natural Language Processing with Small Feed-Forward Networks

Jan A. Botha
Emily Pitler
Anton Bakalov
Alex Salcianu
Ryan Mcdonald
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Copenhagen, Denmark, 2879–2885


We show that small and shallow feedforward 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.