© 2017 by ALTISSIA International s.a.
This dataset contains a number of short texts written by non-native speakers of English. Each participant was asked to provide a short answer to an open-ended question which targeted the proficiency level in which he/she was placed. Each question is therefore labelled with a particular proficiency level, as defined by the Common European Framework of Reference for Languages (CEFR).
Moreover, 299 of the collected answers were also labelled using the CEFR, by a panel of three CEFR-certified examiners. Their labels, as well as a majority-vote label, have been added to each one of these texts.
All texts are encoded in a TEI format.
More information can be found in the following paper. When using the data in your research or publication, please cite this work as well.
@inproceedings{tack-etal-2017-human,
title = {Human and Automated {CEFR}-based Grading of Short Answers},
author = {Tack, Ana{\"\i}s and Fran{\c{c}}ois, Thomas and Roekhaut, Sophie and Fairon, C{\'e}drick},
booktitle = {Proceedings of the 12th Workshop on Innovative Use of {NLP} for Building Educational Applications},
month = sep,
year = {2017},
address = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/W17-5018},
doi = {10.18653/v1/W17-5018},
pages = {169--179}
}
- ALTISSIA International s.a. - www.altissia.com
- Center for Natural Language Processing (CENTAL), Université catholique de Louvain (UCL, Belgium) - [email protected]
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
You should have received a copy of the license along with this work. If not, see https://creativecommons.org/licenses/by-nc-sa/4.0/.
See LICENSE.txt for more details.
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Fixed
- All personal details have been anonymized using the following tags:
- {name}: first or full names
- {initial}: name initials
- {number}: phone numbers
- All personal details have been anonymized using the following tags:
- Added
- First release of the dataset