Machine Translation with Source-Predicted Target Morphology

We propose a novel pipeline for translation into morphologically rich languages which consists of two steps: initially, the source string is enriched with target morphological features and then fed into a translation model which takes care of reordering and lexical choice that matches the provided morphological features. As a proof of concept we first show improved translation performance for a phrase-based model translating source strings enriched with morphological features projected through the word alignments from target words to source words. Given this potential, we present a model for predicting target morphological features on the source string and its predicate-argument structure, and tackle two major technical challenges: (1) How to fit the morphological feature set to training data? and (2) How to integrate the morphology into the back-end phrase-based model such that it can also be trained on projected (rather than predicted) features for a more efficient pipeline? For the first challenge we present a latent variable model, and show that it learns a feature set with quality comparable to a manually selected set for German. And for the second challenge we present results showing that it is possible to bridge the gap between a model trained on a predicted and another model trained on a projected morphologically enriched parallel corpus. Finally we exhibit final translation results showing promising improvement over the baseline phrase-based system.