We describe a scalable decoder for parsing-based machine translation. e decoder is written in Java and 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. Additionally, parallel and distributed computing techniques are exploited to make it scalable. We demonstrate experimentally that our decoder is more than 30 times faster than a baseline decoder written in Python. Large-scale parsing-based statistical machine translation has made significant progress in the last few years. e systems being developed differ in whether they use source-or targetlanguage syntax. For instance, the hierarchical translation system of Chiang (2007) A critical component in parsing-based MT systems is the decoder, which is complex to implement and scale up for large data sets. Most of the systems described above employ tailormade, dedicated decoders that are not open-source, which results in a high barrier to entry for other researchers in the field. However, with the algorithms proposed in (Huang and Chiang, 2005 , Chiang, 2007 , Huang and Chiang, 2007 , it is possible to develop a general-purpose decoder that can be used by all the parsing-based systems. In this paper, we describe an important first-step towards an extensible, general-purpose, scalable, and open-source parsingbased MT decoder. Our decoder is written in Java and implements all the essential algorithms described in (Chiang, 2007) : chart-parsing, n-gram language model integration, beam-and
Good descriptions of the cube pruning implementation in the Joshua decoder have been provided by Li and Khudanpur (2008) and REF .
Decoding in Joshua: Open Source, Parsing-Based Machine Translation