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Natural Language Processing with Small Feed-Forward Networks
- Publication Year :
- 2017
-
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.<br />Comment: EMNLP 2017 short paper
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1708.00214
- Document Type :
- Working Paper