DOI resolved by resea

Moses

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…

Philipp Koehn, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, Evan Herbst, Hieu Hoang, Alexandra Birch
https://resea.org/10.3115/1557769.1557821

Abstract

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.