Events Calendar

AI Seminar

Speaker: Liang Huang, City University of New York (CUNY)

Talk Title: Scalable Training for Machine Translation Made Successful for the First Time

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: they only train on the small dev set with an impoverished set of rather “dense” features. We instead present a very simple yet theoretically motivated approach by extending my recent framework of “violation-fixing perceptron” to the latent variable setting, and use forced decoding to compute the target derivations. Our method allows structured learning to scale, for the first time, to a large portion of the training data, which enables a rich set of sparse, lexicalized, and non-local features. Extensive experiments show very significant gains in BLEU (by at least +2.0) over MERT and PRO baselines with the help of over 20M sparse features.

Biography: 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).

Host: David Chiang

Location: Information Science Institute (ISI) - 6th floor conference room

Audiences: Everyone Is Invited

Contact: Kary LAU