Max-Violation Perceptron and Forced Decoding for Scalable MT Training

  • Liang Huang | The City University of New York (CUNY)

While large-scale discriminative training has triumphed in many NLP problems, its definite success on machine translation has been largely elusive. Most recent efforts along this line are not scalable (training on the small dev set with features from top ∼100 most fre- quent words) and overly complicated. We instead present a very simple yet theoretically motivated approach by extending the recent framework of “violation-fixing perceptron”, using forced decoding to compute the target derivations. Extensive phrase-based translation experiments on both Chinese-to-English and Spanish-to-English tasks show substantial gains in BLEU by up to +2.3/+2.0 on dev/test over MERT, thanks to 20M+ sparse features. This is the first successful effort of large-scale online discriminative training for MT.

Speaker Details

Liang Huang is currently an Assistant Professor at the City University of New York (CUNY). He graduated in 2008 from Penn and has worked as a Research Scientist at Google and a Research Assistant Professor at USC/ISI. His work is mainly on the theoretical aspects (algorithms and formalisms) of computational linguistics, and related theoretical problems in machine learning. He has received a Best Paper Award at ACL 2008, several best paper nominations (ACL 2007, EMNLP 2008, and ACL 2010), two Google Faculty Research Awards (2010 and 2013), and a University Graduate Teaching Prize at Penn (2005).

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      Jeff Running

Series: Microsoft Research Talks