Time: 2:15pm-3:30pm Place: Room C415A (basement level), CUNY Graduate Center (365 5th Ave, between 34str. & 35str) Speaker: Heng Yu, Chinese Academy of Sciences and City University of New York Title: Max-Violation Perceptron and Forced Decoding for Scalable MT Training Joint work with Liang Huang (CUNY), Haitao Mi (IBM), and Kai Zhao (CUNY). Paper: http://acl.cs.qc.edu/~lhuang/papers/maxforce.pdf Abstract: 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. Bio: Heng Yu is a fifth-year Ph.D. student under Prof. Qun Liu at the Institute of Computing Technologies, Chinese Academy of Sciences (CAS/ICT). Since May 2013 he has been visiting the Algorithms for Computational Linguistics (ACL) Group at CUNY led by Prof. Liang Huang. His research is on machine translation, in particular, large-scale discriminative training and syntactic language models.