Correcting ESL Errors Using Phrasal SMT Techniques

This paper presents a pilot study of the use of phrasal Statistical Machine Translation (SMT) techniques to identify and correct writing errors made by learners of English as a Second Language (ESL). Using examples of mass noun errors found in the Chinese Learner Error Corpus (CLEC) to guide creation of an engineered training set, we show that application of the SMT paradigm can capture errors not well addressed by widely-used proofing tools designed for native speakers. Our system was able to correct

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Chris Brockett, William B. Dolan, Michael Gamon
• Keywords: ACL 2006, ACL 2007, Learner Error Corpus, Mass Noun Errors, Phrasal Statistical Machine