Statistical machine translation : from single-word models to alignment templates

In this work, new approaches for machine translation using statistical methods are described. In addition to the standard source-channel approach to statistical machine translation, a more general approach based on the maximum entropy principle is presented. Various methods for computing single-word alignments using statistical or heuristic models are described. Various smoothing techniques, methods to integrate a conventional dictionary and training methods are analyzed. A detailed evaluation of these models is performed by comparing the automatically produced word alignment with a manually produced reference alignment. Based on these fundamental single-word based alignment models, a new phrase-based translation model - the alignment template model - is suggested. For this model, a training and an efficient search algorithm is developed. For two specific applications (interactive translation and multi-source translation) specific search algorithms are developed. The suggested machine translation approach has been tested for the German-English Verbmobil task, the French-English Hansards task and for Chinese-English news text translation. Often, the obtained results have been significantly better than those obtained with alternative approaches to machine translation.

Grantor dc:publisher

Publikationsserver der RWTH Aachen University

Year dc:date

2002

Author dc:creator

• Och, Franz Josef

Contributors dc:contributor

• Ney, Hermann

• info:eu-repo/classification/ddc/004
• Informatik
• statistical machine translation
• alignment models
• maximum entropy

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

eng

Identifier

https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-120579%22

OAI identifier oai:identifier

oai:publications.rwth-aachen.de:58741