Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task

Kenton Murray, Brian DuSell, David Chiang

[How to correct problems with metadata yourself]

Abstract
This paper describes the Notre Dame Natural Language Processing Group’s (NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019). We investigated the impact of auto-sizing (Murray and Chiang, 2015; Murray et al., 2019) to the Transformer network (Vaswani et al., 2017) with the goal of substantially reducing the number of parameters in the model. Our method was able to eliminate more than 25% of the model’s parameters while suffering a decrease of only 1.1 BLEU.
Anthology ID:
D19-5634
Volume:
Proceedings of the 3rd Workshop on Neural Generation and Translation
Month:
November
Year:
2019
Address:
Hong Kong
Editors:
Alexandra Birch, Andrew Finch, Hiroaki Hayashi, Ioannis Konstas, Thang Luong, Graham Neubig, Yusuke Oda, Katsuhito Sudoh
Venue:
NGT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
297–301
Language:
URL:
https://aclanthology.org/D19-5634
DOI:
10.18653/v1/D19-5634
Bibkey:
Cite (ACL):
Kenton Murray, Brian DuSell, and David Chiang. 2019. Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task. In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 297–301, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task (Murray et al., NGT 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D19-5634.pdf
PDF Search