|Date||Apr 20, 2015|
|Title||Two Methods for Exploiting Richer Source Information in SMT|
In the last two years, models that exploit richer information from the source sentence have significantly improved the quality of outputs from statistical machine translation (SMT) systems. At the National Research Council of Canada (NRC), we have recently implemented two such models – one homegrown, one a reimplementation of someone else’s work.
|Bio||Since Jan. 2014, Roland Kuhn has been Team Leader for the Multilingual Text Processing group at the National Research Council of Canada (NRC). He has been Co-Leader of the Portage machine translation project at NRC since July 2004. After studying mathematical biology at the University of Toronto and the University of Chicago, Dr. Kuhn developed an interest in natural language. In 1993, he received his Ph.D. in Computer Science from McGill University. He worked at the Centre de recherche informatique de Montréal (CRIM) as a researcher and a senior researcher between 1992 and 1996, then held research and development positions with Panasonic Speech Technology Laboratory in Santa Barbara, California from October 1996 to June 2004. He joined the National Research Council of Canada (NRC) in 2004. In the course of his research career, he has studied a diverse set of problems in natural language processing, including automatic speech recognition and machine translation.|