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 example of a phrase-pair in the corpus with respect to the input and combines them into dynamic collections of examples. 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 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.