Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation
Kenton Murray, Jeffery Kinnison, Toan Q. Nguyen, Walter Scheirer, David Chiang
Abstract
Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation. Yet these neural networks are very sensitive to architecture and hyperparameter settings. Optimizing these settings by grid or random search is computationally expensive because it requires many training runs. In this paper, we incorporate architecture search into a single training run through auto-sizing, which uses regularization to delete neurons in a network over the course of training. On very low-resource language pairs, we show that auto-sizing can improve BLEU scores by up to 3.9 points while removing one-third of the parameters from the model.- Anthology ID:
- D19-5625
- Volume:
- Proceedings of the 3rd Workshop on Neural Generation and Translation
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong
- Venue:
- NGT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 231–240
- Language:
- URL:
- https://aclanthology.org/D19-5625
- DOI:
- 10.18653/v1/D19-5625
- Cite (ACL):
- Kenton Murray, Jeffery Kinnison, Toan Q. Nguyen, Walter Scheirer, and David Chiang. 2019. Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation. In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 231–240, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation (Murray et al., NGT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D19-5625.pdf
- Code
- KentonMurray/ProxGradPytorch