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} } |