Paper Info
Title
Joshua 2.0: a toolkit for parsing-based machine translation with syntax, semirings, discriminative training and other goodies
Abstract
We describe the progress we have made in the past year on Joshua (Li et al., 2009a), an open source toolkit for parsing based machine translation. The new functionality includes: support for translation grammars with a rich set of syntactic nonterminals, the ability for external modules to posit constraints on how spans in the input sentence should be translated, lattice parsing for dealing with input uncertainty, a semiring framework that provides a unified way of doing various dynamic programming calculations, variational decoding for approximating the intractable MAP decoding, hypergraph-based discriminative training for better feature engineering, a parallelized MERT module, documentlevel and tail-based MERT, visualization of the derivation trees, and a cleaner pipeline for MT experiments.
Year
Venue
Keywords
2010
WMT@ACL
input uncertainty,parsing-based machine translation,input sentence,variational decoding,better feature engineering,translation grammar,intractable map decoding,machine translation,discriminative training,tail-based mert,parallelized mert module,mt experiment
Field
DocType
Citations 
Rule-based machine translation,Computer science,Visualization,Machine translation,Feature engineering,Transfer-based machine translation,Natural language processing,Artificial intelligence,Parsing,Discriminative model,Syntax
Conference
14
PageRank 
References 
Authors
0.99
19
11
Name
Order
Citations
PageRank
Zhifei Li133923.83
Chris Callison-Burch24872259.75
chris dyer35438232.28
Juri Ganitkevitch465932.71
Ann Irvine521915.23
Sanjeev Khudanpur62155202.00
Lane Schwartz720918.01
Wren N. G. Thornton81247.74
Ziyuan Wang913113.42
Jonathan Weese1032519.11
Omar Zaidan1178743.28