Paper: Discriminative Training And Maximum Entropy Models For Statistical Machine Translation

ACL ID P02-1038
Title Discriminative Training And Maximum Entropy Models For Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

We present a framework for statistical machine translation of natural languages based on direct maximum entropy mod- els, which contains the widely used sour- ce-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 transla- tion system is significantly improved us- ing this approach.