Combining outputs from multiple machine translation systems – Antti-Veikko Rosti (BBN)
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The interest in system combination for machine translation has recently increased due to programs involving multiple sites. In programs, such as the DARPA GALE, the sites develop MT systems independently for the same task. As these systems have different strengths and only a single output for each task is evaluated, several methods to combine the outputs from all systems to leverage their strengths have been explored. The system combination efforts within the AGILE team from the beginning of the GALE program until the recent re-test are presented in this talk. The talk will cover topics from two recent papers presented at the 2007 NAACL-HLT and ACL conferences as well as the latest improvements developed for the GALE Phase 2 re-test.
Related papers: http://acl.ldc.upenn.edu/N/N07/N07-1029.pdf http://acl.ldc.upenn.edu/P/P07/P07-1040.pdf
Antti-Veikko Rosti received his MSc in information technology from Tampere University of Technology, Finland, and PhD in information engineering from Cambridge University, UK. He joined IBM Research as a postdoctoral researcher in Yorktown Heights, NY in 2004. Since 2005 he has been a scientist at BBN Technologies in Cambridge, MA. His research interests are in statistical signal processing and machine learning with a particular emphasis on their application to audio, speech, and language processing.