Unsupervised discriminative language model training for machine translation using simulated confusion sets
Zhifei Li, Ziyuan Wang, Sanjeev P. Khudanpur, Jason M. Eisner · 2010
An unsupervised discriminative training procedure is proposed for estimating a language model (LM) for machine translation (MT). An English-to-English synchronous context-free grammar is derived from a baseline MT system to capture translation alternatives: pairs of words, phrases or other sentence fragments that potentially compete to be the translation of the same source-language fragment. Using this grammar, a set of impostor sentences is then created for each English sentence to simulate confusions that would arise if the system were to process