We present a novel approach to training discriminative tree-structured machine trans- lation systems by learning to search. We describe three primary innovations in this work: a new parsing coordinator architecture and algorithms to generate 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 our innovations.
|Commitee:||Bangalore, Srinivas, Grishman, Ralph, Mohri, Mehryar, Sekine, Satoshi|
|School:||New York University|
|School Location:||United States -- New York|
|Source:||DAI-B 74/04(E), Dissertation Abstracts International|
|Keywords:||Computational linguistics, Machine learning, Machine translation, Natural language processing|
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