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Achieving Domain Specificity in SMT without Overt Siloing
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Achieving Domain Specificity in SMT without Overt Siloing
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.
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