Detection of non-native sentences using machine-translated training data

John Lee; Ming Zhou; Xiaohua Liu

doi:10.3115/1614108.1614132

Detection of non-native sentences using machine-translated training data

John Lee
, Ming Zhou
, Xiaohua Liu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

11 Citations (Scopus)

Abstract

Training statistical models to detect nonnative sentences requires a large corpus of non-native writing samples, which is often not readily available. This paper examines the extent to which machinetranslated (MT) sentences can substitute as training data. Two tasks are examined. For the native vs non-native classification task, nonnative 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.

Original language	English
Title of host publication	NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers
Publisher	Association for Computational Linguistics
Pages	93-96
DOIs	https://doi.org/10.3115/1614108.1614132
Publication status	Published - 2007
Externally published	Yes
Event	2007 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT 2007 - Rochester, United States Duration: 22 Apr 2007 → 27 Apr 2007 https://aclanthology.org/N07-2

Publication series

Name	NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers

Conference

Conference	2007 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT 2007
Place	United States
City	Rochester
Period	22/04/07 → 27/04/07
Internet address	https://aclanthology.org/N07-2

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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Lee, J., Zhou, M., & Liu, X. (2007). Detection of non-native sentences using machine-translated training data. In NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers (pp. 93-96). (NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers). Association for Computational Linguistics. https://doi.org/10.3115/1614108.1614132

Lee, John ; Zhou, Ming ; Liu, Xiaohua. / Detection of non-native sentences using machine-translated training data. NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers. Association for Computational Linguistics, 2007. pp. 93-96 (NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers).

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title = "Detection of non-native sentences using machine-translated training data",

abstract = "Training statistical models to detect nonnative sentences requires a large corpus of non-native writing samples, which is often not readily available. This paper examines the extent to which machinetranslated (MT) sentences can substitute as training data. Two tasks are examined. For the native vs non-native classification task, nonnative 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.",

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Lee, J, Zhou, M & Liu, X 2007, Detection of non-native sentences using machine-translated training data. in NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers. NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers, Association for Computational Linguistics, pp. 93-96, 2007 Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT 2007, Rochester, United States, 22/04/07. https://doi.org/10.3115/1614108.1614132

Detection of non-native sentences using machine-translated training data. / Lee, John; Zhou, Ming; Liu, Xiaohua.
NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers. Association for Computational Linguistics, 2007. p. 93-96 (NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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PY - 2007

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AB - Training statistical models to detect nonnative sentences requires a large corpus of non-native writing samples, which is often not readily available. This paper examines the extent to which machinetranslated (MT) sentences can substitute as training data. Two tasks are examined. For the native vs non-native classification task, nonnative 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.

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Lee J, Zhou M, Liu X. Detection of non-native sentences using machine-translated training data. In NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers. Association for Computational Linguistics. 2007. p. 93-96. (NAACL-HLT 2007 - Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Companion Volume: Short Papers). doi: 10.3115/1614108.1614132

Detection of non-native sentences using machine-translated training data

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