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...

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