PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models
Chris Dyer, Adam Lopez, Juri Ganitkevitch, Jonathen Weese, Ferhan Ture, Phil Blunsom, Hendra Setiawan, Vlad Eidelman and Philip Resnik
In: Proceedings of the ACL 2010 System Demonstrations, 11-16 July 2010, Uppsala, Sweden.

Abstract

We present cdec, an open source frame- work 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 gram- mars. 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 transla- tions, but also alignments to a reference, or the quantities necessary to drive dis- criminative 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7945
Deposited By:Phil Blunsom
Deposited On:17 March 2011