Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs

Autor: Kerz, E., Wiechmann, D., Qiao, Y., Tseng, E., Ströbel, M., Burstein, J., Horbach, A., Kochmar, E., Laarmann-Quante, R., Leacock, C., Madnani, N., Pilán, I., Yannakoudakis, H., Zesch, T.

Publikováno v: Innovative Use of NLP for Building Educational Applications: EACL 2021 : proceedings of the 16th workshop : April 20, 2021, 199-209
STARTPAGE=199;ENDPAGE=209;TITLE=Innovative Use of NLP for Building Educational Applications

Automatically predicting the level of second language (L2) learner proficiency is an emerging topic of interest and research based on machine learning approaches to language learning and development. The key to the present paper is the combined use o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::46f61767142dac33d69c66bb495cdf64
https://dare.uva.nl/personal/pure/en/publications/automated-classification-of-written-proficiency-levels-on-the-cefrscale-through-complexity-contours-and-rnns(3db2076d-a8cd-4113-b602-035bec6304a6).html

HOO 2012 Error Recognition and Correction Shared Task: Cambridge University Submission Report

Autor: Kochmar, E, Andersen, O, Briscoe, E

Previous work on automated error recognition and correction of texts written by learners of English as a Second Language has demonstrated experimentally that training classifiers on error-annotated ESL text generally outperforms training on native te

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6415fb6c70270a75d3cc5a7e88923710

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