Text prediction with fuzzy alignment

George Foster, Philippe Langlais, Guy Lapalme


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:
Bibkey:
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PDF:
https://link.springer.com/chapter/10.1007/3-540-45820-4_5