Cunei is a hybrid platform for machine
translation that draws upon the depth of research in Example-Based MT (EBMT)
and Statistical MT (SMT). In particular, Cunei uses a data-driven approach
that extends upon the basic thesis of EBMT--that some examples in the
training data are of higher quality or are more relevant than
others. Yet, it does so in a statistical manner, embracing much of the
modeling pioneered by SMT, allowing for efficient
optimization. Instead of using a static model for each phrase-pair,
at run-time Cunei models each instance of translation
in the corpus with
respect to the input. Ultimately, this approach provides a more consistent model
and a more flexible framework for integration of novel run-time features.
Want to know more? Read one of our papers:
Aaron B. Phillips. "Cunei: Open-Source Machine Translation with Relevance-Based Models of Each Translation Instance." Machine Translation, 25(2):161-177, 2011.
Aaron B. Phillips and Ralf D. Brown. "Training Machine Translation with a Second-Order Taylor Approximation of Weighted Translation Instances." Machine Translation Summit XIII, Xiamen, China, September 2011.
Aaron B. Phillips. "Cunei Machine Translation Platform for WMT'10." The Fifth Workshop on Statistical Machine Translation, Uppsala, Sweden, July 2010.
Aaron B. Phillips and Ralf D. Brown. "Cunei Machine Translation Platform: System Description." 3rd Workshop on Example-Based Machine Translation, Dublin, Ireland, November 2009.
Aaron B. Phillips "Sub-Phrasal Matching and Structural Templates in Example-Based MT." The 11th Conference on Theoretical and Methodological Issues in Machine Translation, Skövde, Sweden, September 2007.