Tuning a Grammar Correction

In this paper, we propose two enhance-ments to a statistical machine translation based approach to grammar correction for correcting all error categories. First, we propose tuning the SMT systems to op-timize a metric more suited to the gram-mar correction task (F-β score) rather than the traditional BLEU metric used for tun-ing language translation tasks. Since the F-β score favours higher precision, tun-ing to this score can potentially improve precision. While the results do not indi-cate improvement due to tuning with the new metric, we believe this could be due to the small number of grammatical er-rors in the tuning corpus and further in-vestigation is required to answer the ques-tion conclusively. We also explore the combination of custom-engineered gram-mar correction techniques, which are tar-geted to specific error categories, with the SMT based method. Our simple ensem-ble methods yield improvements in recall but decrease the precision. Tuning the custom-built techniques can help in in-creasing the overall accuracy also.