Abstract
Statistical Machine Translation (SMT) systems specialized for one domain often perform poorly when applied to other domains. Domain adaptation techniques allow SMT models trained from a source domain with abundant data to accommodate different target domains with limited data. This paper evaluates the performance of two adaptive techniques based on log-linear and mixture models on data from the legal domain in real-world settings. Performance evaluation includes post-editing time and effort required by a professional post-editor to improve the quality of machine-generated translations to meet industry standards, as well as traditional automated scoring techniques (BLEU scores). Results indicates that the domain adaptation techniques can yield a significant increase in BLEU score (up to three points) and a significant reduction in post-editing time of about one second per word in an operational environment.- Anthology ID:
- 2012.amta-commercial.6
- Volume:
- Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program
- 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-commercial.6
- DOI:
- Cite (ACL):
- Atefeh Farzindar and Wael Khreich. 2012. Evaluation of Domain Adaptation Techniques for TRANSLI in a Real-World Environment. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program, San Diego, California, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Evaluation of Domain Adaptation Techniques for TRANSLI in a Real-World Environment (Farzindar & Khreich, AMTA 2012)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2012.amta-commercial.6.pdf