Improved Domain Adaptation for Statistical Machine Translation
Wei Wang, Klaus Macherey, Wolfgang Macherey, Franz Och, Peng Xu
Abstract
We present a simple and effective infrastructure for domain adaptation for statistical machine translation (MT). To build MT systems for different domains, it trains, tunes and deploys a single translation system that is capable of producing adapted domain translations and preserving the original generic accuracy at the same time. The approach unifies automatic domain detection and domain model parameterization into one system. Experiment results on 20 language pairs demonstrate its viability.- Anthology ID:
- 2012.amta-papers.18
- Volume:
- Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
- Month:
- October 28-November 1
- Year:
- 2012
- Address:
- San Diego, California, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2012.amta-papers.18
- DOI:
- Cite (ACL):
- Wei Wang, Klaus Macherey, Wolfgang Macherey, Franz Och, and Peng Xu. 2012. Improved Domain Adaptation for Statistical Machine Translation. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Improved Domain Adaptation for Statistical Machine Translation (Wang et al., AMTA 2012)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2012.amta-papers.18.pdf