Using machine learning to improve rule-based machine translation
نویسنده
چکیده
We present an experiment of using transformation-based learning for improving translation quality of a rule-based machine translation system by means of post-processing. Transformation rules are learned based on a parallel corpus of machine translation output and a human-corrected version of the output. The experiment resulted in a significant increase in translation quality of 0.8 measured using the BLEU metric.
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