Paper: HMM-Based Word Alignment In Statistical Translation

ACL ID C96-2141
Title HMM-Based Word Alignment In Statistical Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1996
Authors

In this paper, we describe a new model for word alignment in statistical trans- lation and present experimental results. The idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions. To achieve this goal, the approach us- es a first-order Hidden Markov model (HMM) for the word alignment problem as they are used successfully in speech recognition for the time alignment prob- lem. The difference to the time align- ment HMM is that there is no monotony constraint for the possible word order- ings. We describe the details of the mod- el and test the model on several bilingual corpora.