@inproceedings{horbach-etal-2017-fine,
title = "Fine-grained essay scoring of a complex writing task for native speakers",
author = "Horbach, Andrea and
Scholten-Akoun, Dirk and
Ding, Yuning and
Zesch, Torsten",
editor = "Tetreault, Joel and
Burstein, Jill and
Leacock, Claudia and
Yannakoudakis, Helen",
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/W17-5040",
doi = "10.18653/v1/W17-5040",
pages = "357--366",
abstract = "Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays. We present a new dataset of essays written by highly proficient German native speakers that is scored using a fine-grained rubric with the goal to provide detailed feedback. Our experiments with two state-of-the-art scoring systems (a neural and a SVM-based one) show a large drop in performance compared to existing datasets. This demonstrates the need for such datasets that allow to guide research on more elaborate essay scoring methods.",
}
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<abstract>Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays. We present a new dataset of essays written by highly proficient German native speakers that is scored using a fine-grained rubric with the goal to provide detailed feedback. Our experiments with two state-of-the-art scoring systems (a neural and a SVM-based one) show a large drop in performance compared to existing datasets. This demonstrates the need for such datasets that allow to guide research on more elaborate essay scoring methods.</abstract>
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%0 Conference Proceedings
%T Fine-grained essay scoring of a complex writing task for native speakers
%A Horbach, Andrea
%A Scholten-Akoun, Dirk
%A Ding, Yuning
%A Zesch, Torsten
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F horbach-etal-2017-fine
%X Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays. We present a new dataset of essays written by highly proficient German native speakers that is scored using a fine-grained rubric with the goal to provide detailed feedback. Our experiments with two state-of-the-art scoring systems (a neural and a SVM-based one) show a large drop in performance compared to existing datasets. This demonstrates the need for such datasets that allow to guide research on more elaborate essay scoring methods.
%R 10.18653/v1/W17-5040
%U https://aclanthology.org/W17-5040
%U https://doi.org/10.18653/v1/W17-5040
%P 357-366
Markdown (Informal)
[Fine-grained essay scoring of a complex writing task for native speakers](https://aclanthology.org/W17-5040) (Horbach et al., BEA 2017)
ACL