Improving Domain-specific Machine Translation by Constraining the Language Model
ARMY RESEARCH LAB ADELPHI MD COMPUTATIONAL AND INFORMATION SCIENCES DIRECTORATE
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A domain-specific statistical machine translation engine is shown to be more accurate when only domain-specific language data are used to build the target-language language model. This has been found to be true when compared to using a much larger, out-of-domain corpus for building the language model, either alone or in combination with the domain-specific data.