Discourse-aware Statistical Machine Translation as a Context-sensitive Spell Checker

Real-word errors or context sensitive spelling errors, are misspelled words that have been wrongly converted into another word of vocabulary. One way to detect and correct real-word errors is using Statistical Machine Translation (SMT), which translates a text containing some real-word errors into a correct text of the same language. In this paper, we improve the results of mentioned SMT system by employing some discourseaware features into a log-linear reranking method. Our experiments on a real-world test data in Persian show an improvement of about 9.5% and 8.5% in the recall of detection and correction respectively. Other experiments on standard English test sets also show considerable improvement of real-word checking results.