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Abstract
In this talk I’ll outline current work at the University of Edinburgh to model machine translation (MT) as a probabilistic machine learning problem.Although MT systems have made large gains in translation quality in recent years, most are currently induced using a hand engineered pipeline of disparate models linked by heuristics. Although such techniques are effective for translating between related languages, they fail to capture the latent structure necessary to translate between languages which diverge significantly in word order, such as Chinese and English.I’ll present a non-parametric Bayesian model for inducing synchronous context free grammars capable of learning the latent structure of translation equivalence from a corpus of parallel string pairs. I’ll discuss the efficacy of both variational Bayes and Gibbs sampling inference procedures for this model and present experiments demonstrating competitive results on full scale translation evaluations.
Biography
Phil Blunsom is a Research Fellow in the Institute for Communicating and Collaborative Systems at the University of Edinburgh. He completed his PhD at the University of Melbourne in 2007. His current research interests focus upon the application of machine learning to complex structured problems in language processing, such as machine translation, language modelling, parsing and grammar induction.