Open Source Toolkit for Statistical Machine Translation

The objective of this JHU Workshop is the development of novel methods for statistical machine translation that improve the state of the art, specifically factored translation models, and lattice-based decoding methods. As part of this workshop, we will implement these techniques and distribute them in an open source toolkit.

We propose to extend phrase-based statistical machine translation models using a factored representation. Current statistical MT approaches represent each word simply as their textual form. A factored translation approach replaces this representation with a feature vector for each word derived from a variety of information sources. These features may be the surface form, lemma, stem, part-of-speech tag, morphological information, syntactic, semantic or automatically derived categories, etc. This representation is then used to construct statistical translation models that can be combined together to maximize translation quality.

We also propose to extend current MT decoding methods to process multiple, ambiguous hypotheses in the form of an input lattice. A lattice representation allows an MT system to arbitrate between multiple ambiguous hypotheses from upstream processing so that the best translation can be produced. During the workshop we will implement lattice decoding and run experiments with errorful ASR input. We will compare different lattice-based strategies against single-hypothesis input results.

Final Report
Find details about the plans and progress of this project here.

 

Team Members 
Senior Members
Chris Callison-BurchCLSP
Nicola BertoldiITC-IRST
Marcello FedericoITC-IRST
Philipp KoehnUniversity of Edinburgh
Wade ShenLincoln Labs
Graduate Students
Ondrej BojarCharles University
Brooke CowanMIT
Chris DyerUniversity of Maryland
Hieu HoangUniversity of Edinburgh
Richard ZensAachen University
Undergraduate Students
Alexandra ConstantinWilliams College
Evan HerbstCornell
Christine Corbett MoranMIT

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2608

Center for Language and Speech Processing