Paper: Correcting ESL Errors Using Phrasal SMT Techniques

ACL ID P06-1032
Title Correcting ESL Errors Using Phrasal SMT Techniques
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

This paper presents a pilot study of the use of phrasal Statistical Machine Trans- lation (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 Cor- pus (CLEC) to guide creation of an engi- neered training set, we show that applica- tion of the SMT paradigm can capture er- rors not well addressed by widely-used proofing tools designed for native speak- ers. 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, suggest- ing that efforts to collect alignable cor- pora of pre- and post-editing ESL writing samples offer can enable the develop- ment of SMT-based writing assistance to...