Paper: Tuning a Grammar Correction System for Increased Precision

ACL ID W14-1708
Title Tuning a Grammar Correction System for Increased Precision
Venue International Conference on Computational Natural Language Learning
Session shared task
Year 2014

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-? score) rather than the traditional BLEU metric used for tun- ing language translation tasks. Since the F-? 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 tec...