Candidate: Daniel Galron
Advisor: Dan Melamed
Optimizing Machine Translation by Learning to Search
We present a novel approach to training discriminative tree-structured machine translation systems by learning to search. We describe three primary innovations in this work: a new parsing coordinator architecture and algorithms to synthesize the required training examples for the learning algorithm; a new semiring that provides an unbiased way to compare translations; and a new training objective that measures whether a translation inference improves the quality of a translation. We also apply the reinforcement learning concept of exploration to SMT. Finally, we empirically evaluate the effects of our innovations on the quality of translations output by our system.