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.
['Zhifei Li', 'Chris Callison-Burch', 'Chris Dyery', 'Juri Ganitkevitch', 'Sanjeev Khudanpur', 'Lane Schwartz', 'Wren N. G. Thornton', 'Jonathan Weese', 'Omar F. Zaidan']
Demonstration of Joshua: An Open Source Toolkit for Parsing-based Machine Translation