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2010

cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models

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cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models
We present cdec, an open source framework for decoding, aligning with, and training a number of statistical machine translation models, including word-based models, phrase-based models, and models based on synchronous context-free grammars. Using a single unified internal representation for translation forests, the decoder strictly separates model-specific translation logic from general rescoring, pruning, and inference algorithms. From this unified representation, the decoder can extract not only the 1- or k-best translations, but also alignments to a reference, or the quantities necessary to drive discriminative training using gradient-based or gradient-free optimization techniques. Its efficient C++ implementation means that memory use and runtime performance are significantly better than comparable decoders.
Chris Dyer, Adam Lopez, Juri Ganitkevitch, Jonatha
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ACL
Authors Chris Dyer, Adam Lopez, Juri Ganitkevitch, Jonathan Weese, Ferhan Türe, Phil Blunsom, Hendra Setiawan, Vladimir Eidelman, Philip Resnik
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