Fast, Scalable Phrase-Based SMT Decoding
Hieu Hoang, Nikolay Bogoychev, Lane Schwartz, Marcin Junczys-Dowmunt
Abstract
The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community. As commercial use has increased, there is need for software that is optimized for commercial requirements, in particular, fast phrase-based decoding and more efficient utilization of modern multicore servers. In this paper we re-examine the major components of phrase-based decoding and decoder implementation with particular emphasis on speed and scalability on multicore machines. The result is a drop-in replacement for the Moses decoder which is up to fifteen times faster and scales monotonically with the number of cores.- Anthology ID:
- 2016.amta-researchers.4
- Volume:
- Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track
- Month:
- October 28 - November 1
- Year:
- 2016
- Address:
- Austin, TX, USA
- Editors:
- Spence Green, Lane Schwartz
- Venue:
- AMTA
- SIG:
- Publisher:
- The Association for Machine Translation in the Americas
- Note:
- Pages:
- 40–52
- Language:
- URL:
- https://aclanthology.org/2016.amta-researchers.4
- DOI:
- Cite (ACL):
- Hieu Hoang, Nikolay Bogoychev, Lane Schwartz, and Marcin Junczys-Dowmunt. 2016. Fast, Scalable Phrase-Based SMT Decoding. In Conferences of the Association for Machine Translation in the Americas: MT Researchers' Track, pages 40–52, Austin, TX, USA. The Association for Machine Translation in the Americas.
- Cite (Informal):
- Fast, Scalable Phrase-Based SMT Decoding (Hoang et al., AMTA 2016)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2016.amta-researchers.4.pdf