System Combination for Grammatical Error Correction

Different approaches to high-quality grammatical error correction have been proposed recently, many of which have their own strengths and weaknesses. Most of these approaches are based on classification or statistical machine translation (SMT). In this paper, we propose to combine the output from a classification-based system and an SMT-based system to improve the correction quality. We adopt the system combination technique of Heafield and Lavie (2010). We achieve an F0.5 score of 39.39% on the test set of the CoNLL-2014 shared task, outperforming the best system in the shared task.