Statistical machine translation toolkits like Moses have not been designed with grammatical error correction in mind. In order to achieve competitive results in this area, it is not enough to simply add more data. Optimization procedures need to be customized, task-specific features should be introduced. Only then can the decoder take advantage of relevant data. We demonstrate the validity of the above claims by combining web-scale language models and large-scale error-corrected texts with parameter tuning according to the task metric and correction-specific features. Our system achieves a result of 35.0% F_(0.5) on the blind CoNLL-2014 test set, ranking on third place. A similar system, equipped with identical models but without tuned parameters and specialized features, stagnates at 25.4%.
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