Automated Grammar Correction Using Hierarchical Phrase-Based Statistical Machine Translation

We introduce a novel technique that uses hierarchical phrase-based statistical machine translation (SMT) for grammar correction. SMT systems provide a uniform platform for any sequence transformation task. Thus grammar correction can be considered a translation problem from incorrect text to correct text. Over the years, grammar correction data in the electronic form (i.e., parallel corpora of incorrect and correct sentences) has increased manifolds in quality and quantity, making SMT systems feasible for grammar correction. Firstly, sophisticated translation models like hierarchical phrase-based SMT can handle errors as complicated as reordering or insertion, which were difficult to deal with previously throuh the mediation of rule based systems. Secondly, this SMT based correction technique is similar in spirit to human correction, because the system extracts grammar rules from the corpus and later uses these rules to translate incorrect sentences to correct sentences. We describe how to use Joshua, a hierarchical phrase-based SMT system for grammar correction. An accuracy of 0.77 (BLEU score) establishes the efficacy of our approach.