Moses: Open Source Toolkit for Statistical Machine Translation

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks. 1 Motivation Phrase-based statistical machine translation (Koehn et al. 2003) has emerged as the dominant paradigm in machine translation research. However, until now, most work in this field has been carried out on proprietary and in-house research systems. This lack of openness has created a high barrier to entry for researchers as many of the components required have had to be duplicated. This has also hindered effective comparisons of the different elements of the systems. By providing a free and complete toolkit, we hope that this will stimulate the development of the field. ...

• Type: Conference
• Year: 2007
• Where: ACL
• Authors: Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, Evan Herbst
• Keywords: ACL 2007, Computational Linguistics, Confusion Network, Phrase-based Statistical Machine, Statistical Machine Translation