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Title |
Collecting SMT Language Model Training Data for Low Source Language |
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Author |
Mamtily Nighmat and Izumi Yamamoto |
| Citation |
Vol. 17 No. 11 pp. 103-107 |
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Abstract |
Statistical machine translation (SMT) system basically relies on parallel corpus [1]. Different than Rule based Machine translation (RBMT) approach, capability of SMT system almost depends under the size of corpus. The quality of corpus became a key to build better SMT translation system. In this work, parallel corpus [2] in three languages translated to Uyghur language one by one manually evaluated and applied as train data for Uyghur language model. As a conclusion, comparison between parallel corpus in different grammatical structure language and similar structure language has been discussed. |
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Keywords |
Machine Translation, SMT, Parallel Corpus. |
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