Achieving Domain Specificity in SMT without Overt Siloing
7785Authors
nan
Abstract
AbstractWe 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.
Keywords
Full Content
nan
Labeled Text
AbstractWe 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.
Publication Details
- Paper ID:
- 7785
- Year:
- 2010
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan