Scalable Decoding for Syntax based SMT

Title: Scalable Decoding for Syntax based SMT

Speaker: Abhaya Agarwal
Joint work with Vamshi Ambati

Date: June 10, 2008
Time: 12am - 1:30pm
Location: NSH 3305

Abstract:
Most large scale SMT systems these days use a hybrid model, combining a handful of generative models in a discriminative log-linear framework whose weights are trained on a small amount of development data. Attempts to train fully discriminative MT models that use large number of features, face difficult scalability challenges because it requires repeated decoding of a large training set. In this talk, I will describe a decoder that aims to be efficient enough to allow such training and some initial experiments on training with large number of features.