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
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.- Anthology ID:
- D17-1309
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2879–2885
- Language:
- URL:
- https://aclanthology.org/D17-1309
- DOI:
- 10.18653/v1/D17-1309
- Cite (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.
- Cite (Informal):
- Natural Language Processing with Small Feed-Forward Networks (Botha et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D17-1309.pdf