Abstract
In this paper, we introduce a simple technique for incorporating domain information into a statistical machine translation system that significantly improves translation quality when test data comes from multiple domains. Our approach augments (conjoins) standard translation model and language model features with domain indicator features and requires only minimal modifications to the optimization and decoding procedures. We evaluate our method on two language pairs with varying numbers of domains, and observe significant improvements of up to 1.0 BLEU.- Anthology ID:
- 2012.amta-papers.4
- 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.4
- DOI:
- Cite (ACL):
- Jonathan Clark, Alon Lavie, and Chris Dyer. 2012. One System, Many Domains: Open-Domain Statistical Machine Translation via Feature Augmentation. 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):
- One System, Many Domains: Open-Domain Statistical Machine Translation via Feature Augmentation (Clark et al., AMTA 2012)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2012.amta-papers.4.pdf