Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
Gonzalo Iglesias, William Tambellini, Adrià De Gispert, Eva Hasler, Bill Byrne
Abstract
We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers. We also discuss acceleration strategies for deployment, and the effect of the beam size and batching on memory and speed.- Anthology ID:
- N18-3013
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans - Louisiana
- Editors:
- Srinivas Bangalore, Jennifer Chu-Carroll, Yunyao Li
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 106–113
- Language:
- URL:
- https://aclanthology.org/N18-3013
- DOI:
- 10.18653/v1/N18-3013
- Cite (ACL):
- Gonzalo Iglesias, William Tambellini, Adrià De Gispert, Eva Hasler, and Bill Byrne. 2018. Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 106–113, New Orleans - Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment (Iglesias et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-3013.pdf