Systems Combination for Grammatical Error Correction

Abstract

Different approaches to high-quality grammatical error correction (GEC) have been proposed recently. Most of these approaches are based on classification or statistical machine translation (SMT), each having its own strengths and weaknesses. In this work, we propose to exploit the strengths of multiple GEC systems by system combination. In particular, we 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). Using our approach, 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 by 2.06% (absolute increase).