Demonstration of Joshua: an open source toolkit for parsing-based machine translation

Authors:
Zhifei Li;Chris Callison-Burch;Chris Dyer;Juri Ganitkevitch;Sanjeev Khudanpur;Lane Schwartz;Wren N. G. Thornton;Jonathan Weese;Omar F. Zaidan
Affiliations:
Johns Hopkins University;Johns Hopkins University;University of Maryland;RWTH Aachen University;Johns Hopkins University;University of Minnesota;Johns Hopkins University;Johns Hopkins University;Johns Hopkins University
Venue:
ACLDemos '09 Proceedings of the ACL-IJCNLP 2009 Software Demonstrations
Year:
2009

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Abstract

We describe Joshua (Li et al., 2009a), an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for translation via synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam- and cube-pruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We also provide a demonstration outline for illustrating the toolkit's features to potential users, whether they be newcomers to the field or power users interested in extending the toolkit.