|Benjamin Roth, Andrew McCallum, Marc Dymetman, Nicola Cancedda|
|AMTA (Association for Machine Translation in the Americas), October 31 - November 5, 2010, Denver, Colorado, USA Full paper available on AMTA Website|
We present a conditional-random-field approach to discriminatively-trained phrasebased machine translation in which training and decoding are both cast in a sampling framework and are implemented uniformly in a new probabilistic programming language for factor graphs. In traditional phrase-based translation, decoding infers both a “Viterbi” alignment and the target sentence. In contrast,in our approach, a rich overlappingphrase alignment is produced by a fast deterministic method, while probabilistic decoding infers only the target sentence, which is then able to leverage arbitrary features of the entire source sentence, target sentence and alignment. By using SampleRank for learning we could in principle efficiently estimate hundreds of thousands of parameters. Testtime decoding is done by MCMC sampling with annealing. To demonstrate the potential of our approach we show preliminary experiments leveraging alignments that may contain overlapping bi-phrases.
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