@inproceedings{xia-etal-2019-automatic,
title = "Automatic learner summary assessment for reading comprehension",
author = "Xia, Menglin and
Kochmar, Ekaterina and
Briscoe, Ted",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1261",
doi = "10.18653/v1/N19-1261",
pages = "2532--2542",
abstract = "Automating the assessment of learner summary provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating non-native reading comprehension and propose three novel approaches to automatically assess the learner summaries. We evaluate our models on two datasets we created and show that our models outperform traditional approaches that rely on exact word match on this task. Our best model produces quality assessments close to professional examiners.",
}
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%0 Conference Proceedings
%T Automatic learner summary assessment for reading comprehension
%A Xia, Menglin
%A Kochmar, Ekaterina
%A Briscoe, Ted
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F xia-etal-2019-automatic
%X Automating the assessment of learner summary provides a useful tool for assessing learner reading comprehension. We present a summarization task for evaluating non-native reading comprehension and propose three novel approaches to automatically assess the learner summaries. We evaluate our models on two datasets we created and show that our models outperform traditional approaches that rely on exact word match on this task. Our best model produces quality assessments close to professional examiners.
%R 10.18653/v1/N19-1261
%U https://aclanthology.org/N19-1261
%U https://doi.org/10.18653/v1/N19-1261
%P 2532-2542
Markdown (Informal)
[Automatic learner summary assessment for reading comprehension](https://aclanthology.org/N19-1261) (Xia et al., NAACL 2019)
ACL
- Menglin Xia, Ekaterina Kochmar, and Ted Briscoe. 2019. Automatic learner summary assessment for reading comprehension. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2532–2542, Minneapolis, Minnesota. Association for Computational Linguistics.