{"id":159599,"date":"2019-01-17T09:57:53","date_gmt":"2019-01-17T17:57:53","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/using-mostly-native-data-to-correct-errors-in-learners-writing-a-meta-classifier-approach\/"},"modified":"2019-01-17T09:57:53","modified_gmt":"2019-01-17T17:57:53","slug":"using-mostly-native-data-to-correct-errors-in-learners-writing-a-meta-classifier-approach","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/using-mostly-native-data-to-correct-errors-in-learners-writing-a-meta-classifier-approach\/","title":{"rendered":"Using Mostly Native Data to Correct Errors in Learners&#8217; Writing: A Meta-Classifier Approach"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We present results from a range of experiments on article and preposition error correction for non-native speakers of English. We first compare a language model and error-specific classifiers (all trained on large English corpora) with respect to their performance in error detection and correction. We then combine the language model and the classifiers in a meta-classification approach by combining evidence from the classifiers and the language model as input features to the meta-classifier. The meta-classifier in turn is trained on error-annotated learner data, optimizing the error detection and correction performance on this domain. The meta-classification approach results in substantial gains over the classifier-only and language-model-only scenario. Since the meta-classifier requires error-annotated data for training, we investigate how much training data is needed to improve results over the baseline of not using a meta-classifier. All evaluations are conducted on a large error-annotated corpus of learner English.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present results from a range of experiments on article and preposition error correction for non-native speakers of English. We first compare a language model and error-specific classifiers (all trained on large English corpora) with respect to their performance in error detection and correction. We then combine the language model and the classifiers in a [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"mgamon","user_id":"32888"}],"msr_publishername":"Association for Computational Linguistics","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of NAACL 2010","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of NAACL 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