We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source-channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible hidden variables. This approach allows a baseline machine translation system to be extended easily by adding new feature functions. We show that a baseline statistical machine translation system is significantly improved using this approach.
ABSTRACT
PUBLICATION RECORD
- Publication year
2002
- Venue
Annual Meeting of the Association for Computational Linguistics
- Publication date
2002-07-06
- Fields of study
Linguistics, Computer Science
- Identifiers
- External record
- Source metadata
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