@inproceedings{botha-etal-2017-natural,
title = "Natural Language Processing with Small Feed-Forward Networks",
author = "Botha, Jan A. and
Pitler, Emily and
Ma, Ji and
Bakalov, Anton and
Salcianu, Alex and
Weiss, David and
McDonald, Ryan and
Petrov, Slav",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1309",
doi = "10.18653/v1/D17-1309",
pages = "2879--2885",
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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<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.</abstract>
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%0 Conference Proceedings
%T Natural Language Processing with Small Feed-Forward Networks
%A Botha, Jan A.
%A Pitler, Emily
%A Ma, Ji
%A Bakalov, Anton
%A Salcianu, Alex
%A Weiss, David
%A McDonald, Ryan
%A Petrov, Slav
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F botha-etal-2017-natural
%X 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.
%R 10.18653/v1/D17-1309
%U https://aclanthology.org/D17-1309
%U https://doi.org/10.18653/v1/D17-1309
%P 2879-2885
Markdown (Informal)
[Natural Language Processing with Small Feed-Forward Networks](https://aclanthology.org/D17-1309) (Botha et al., EMNLP 2017)
ACL
- Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan McDonald, and Slav Petrov. 2017. Natural Language Processing with Small Feed-Forward Networks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2879–2885, Copenhagen, Denmark. Association for Computational Linguistics.