Summary of the paper

Title Achieving Domain Specificity in SMT without Overt Siloing
Authors William D. Lewis, Chris Wendt and David Bullock
Abstract We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.
Topics Machine Translation, SpeechToSpeech Translation, Usability, user satisfaction, Tools, systems, applications
Full paper Achieving Domain Specificity in SMT without Overt Siloing
Slides Achieving Domain Specificity in SMT without Overt Siloing
Bibtex @InProceedings{LEWIS10.791,
  author = {William D. Lewis and Chris Wendt and David Bullock},
  title = {Achieving Domain Specificity in SMT without Overt Siloing},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
 }
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