We present an adaptation technique for statistical machine translation, which applies the well-known Bayesian learning paradigm for adapting the model parameters. Since state-of-t...
We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, using classifiers that learn to distinguish human reference tran...
Simon Corston-Oliver, Michael Gamon, Chris Brocket...
We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and...
Ashish Venugopal, Jakob Uszkoreit, David Talbot, F...
A number of machine translation systems based on the learning algorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text...
We have proposed a method of machine translation, which acquires translation rules from translation examples using inductive learning, and have evaluated the method. And we have c...