DocumentCode
2875315
Title
Leveraging multiple languages to improve statistical MT word alignments
Author
Filali, Karim ; Bilmes, Jeff
Author_Institution
Dept. of Comput. Sci. & Eng. & Electr. Eng., Washington Univ., Seattle, WA
fYear
2005
fDate
27-27 Nov. 2005
Firstpage
92
Lastpage
97
Abstract
We present a new multilingual statistical MT word alignment model based on a simple extension of the IBM and HMM models and a two-step alignment procedure. Preliminary results on a small hand-aligned subset of the Europarl corpus show a 7% relative improvement over a state of the art alignment model
Keywords
hidden Markov models; language translation; natural languages; HMM models; hand-aligned subset; leveraging multiple languages; machine translation; statistical MT word alignments; Computer science; Hidden Markov models; Information resources; Mathematics; Natural languages; Optimized production technology; Redundancy; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Automatic Speech Recognition and Understanding, 2005 IEEE Workshop on
Conference_Location
San Juan
Print_ISBN
0-7803-9478-X
Electronic_ISBN
0-7803-9479-8
Type
conf
DOI
10.1109/ASRU.2005.1566493
Filename
1566493
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