Making large-scale support vector machine learning practical
Advances in kernel methods
An intelligent tutoring system for deaf learners of written English
Assets '00 Proceedings of the fourth international ACM conference on Assistive technologies
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A machine learning approach to the automatic evaluation of machine translation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
You're not from 'round here, are you?: naive Bayes detection of non-native utterance text
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatic error detection in the Japanese learners' English spoken data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Automated Japanese essay scoring system based on articles written by experts
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A feedback-augmented method for detecting errors in the writing of learners of English
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Correcting ESL errors using phrasal SMT techniques
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Sentence correction incorporating relative position and parse template language models
IEEE Transactions on Audio, Speech, and Language Processing
ACM Transactions on Asian Language Information Processing (TALIP)
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Training statistical models to detect non-native sentences requires a large corpus of non-native writing samples, which is often not readily available. This paper examines the extent to which machine-translated (MT) sentences can substitute as training data. Two tasks are examined. For the native vs non-native classification task, non-native training data yields better performance; for the ranking task, however, models trained with a large, publicly available set of MT data perform as well as those trained with non-native data.