Paper: An exponential translation model for target language morphology

ACL ID P11-1024
Title An exponential translation model for target language morphology
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

This paper presents an exponential model for translation into highly inflected languages which can be scaled to very large datasets. As in other recent proposals, it predicts target- side phrases and can be conditioned on source- side context. However, crucially for the task of modeling morphological generalizations, it estimates feature parameters from the entire training set rather than as a collection of sepa- rate classifiers. We apply it to English-Czech translation, using a variety of features captur- ing potential predictors for case, number, and gender, and one of the largest publicly avail- able parallel data sets. We also describe gen- eration and modeling of inflected forms un- observed in training data and decoding proce- dures for a model with non-local target-side feature dep...