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Correcting ESL Errors Using Phrasal SMT Techniques

Chris Brockett, William B. Dolan, and Michael Gamon


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 61.81% of mistakes in a set of naturally occurring examples of mass noun errors found on the World Wide Web, suggesting that efforts to collect alignable corpora of pre- and post-editing ESL writing samples can enable the development of SMT-based writing assistance tools capable of repairing many of the complex syntactic and lexical problems found in the writing of ESL learners.


Publication typeInproceedings
Published in21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, Australia
PublisherAssociation for Computational Linguistics
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