Abstract
Text prediction is a form of interactive machine translation that is well suited to skilled translators. In recent work it has been shown that simple statistical translation models can be applied within a usermodeling framework to improve translator productivity by over 10% in simulated results. For the sake of efficiency in making real-time predictions, these models ignore the alignment relation between source and target texts. In this paper we introduce a new model that captures fuzzy alignments in a very simple way, and show that it gives modest improvements in predictive performance without significantly increasing the time required to generate predictions.- Anthology ID:
- 2002.amta-papers.5
- Volume:
- Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers
- Month:
- October 8-12
- Year:
- 2002
- Address:
- Tiburon, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Springer
- Note:
- Pages:
- 44–53
- Language:
- URL:
- https://link.springer.com/chapter/10.1007/3-540-45820-4_5
- DOI:
- Cite (ACL):
- George Foster, Philippe Langlais, and Guy Lapalme. 2002. Text prediction with fuzzy alignment. In Proceedings of the 5th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 44–53, Tiburon, USA. Springer.
- Cite (Informal):
- Text prediction with fuzzy alignment (Foster et al., AMTA 2002)
- PDF:
- https://link.springer.com/chapter/10.1007/3-540-45820-4_5