Wren N. G. Thornton

TL;DR: In this article, a synchronous context free grammars (SCFGs) are used for statistical machine translation. And the toolkit achieves state-of-the-art performance on the WMT09 French-English translation task.

TL;DR: Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam-and cube-pruning, and k-best extraction, and uses parallel and distributed computing techniques for scalability.

TL;DR: A scalable decoder for parsing-based machine translation that implements all the essential algorithms described in (Chiang, 2007) and (Li and Khudanpur, 2008b): chart-parsing, n-gram language model integration, beam- and cube-pruning, and k-best extraction is described.

TL;DR: Support for translation grammars with a rich set of syntactic nonterminals, ability for external modules to posit constraints on how spans in the input sentence should be translated, lattice parsing for dealing with input uncertainty, and a semiring framework that provides a unified way of doing various dynamic programming calculations are described.

TL;DR: Joshua implements all of the algorithms required for translation via synchronous context free grammars: chart-parsing, n-gram language model integration, beam- and cube-pruning, and k-best extraction, and suffix-array grammar extraction and minimum error rate training.