Multi-representation ensembles and delayed SGD updates improve syntax-based NMT
Danielle Saunders, Felix Stahlberg, Adrià de Gispert, Bill Byrne
Abstract
We explore strategies for incorporating target syntax into Neural Machine Translation. We specifically focus on syntax in ensembles containing multiple sentence representations. We formulate beam search over such ensembles using WFSTs, and describe a delayed SGD update training procedure that is especially effective for long representations like linearized syntax. Our approach gives state-of-the-art performance on a difficult Japanese-English task.- Anthology ID:
- P18-2051
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 319–325
- Language:
- URL:
- https://aclanthology.org/P18-2051
- DOI:
- 10.18653/v1/P18-2051
- Cite (ACL):
- Danielle Saunders, Felix Stahlberg, Adrià de Gispert, and Bill Byrne. 2018. Multi-representation ensembles and delayed SGD updates improve syntax-based NMT. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 319–325, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Multi-representation ensembles and delayed SGD updates improve syntax-based NMT (Saunders et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-2051.pdf