Combining off-the-shelf components to clean a translation memory

We present a system to identify erroneous entries in a translation memory. It is a machine learning system that learns to classify entries according to either a strict or a permissive view on correctness. It is trained on features relating to segment length, translation quality checks, spelling and grammar errors, and additionally uses external data for detecting problems with fluency and lexical choice.

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