@inproceedings{yoon-etal-2018-atypical,
title = "Atypical Inputs in Educational Applications",
author = "Yoon, Su-Youn and
Cahill, Aoife and
Loukina, Anastassia and
Zechner, Klaus and
Riordan, Brian and
Madnani, Nitin",
editor = "Bangalore, Srinivas and
Chu-Carroll, Jennifer and
Li, Yunyao",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)",
month = jun,
year = "2018",
address = "New Orleans - Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-3008",
doi = "10.18653/v1/N18-3008",
pages = "60--67",
abstract = "In large-scale educational assessments, the use of automated scoring has recently become quite common. While the majority of student responses can be processed and scored without difficulty, there are a small number of responses that have atypical characteristics that make it difficult for an automated scoring system to assign a correct score. We describe a pipeline that detects and processes these kinds of responses at run-time. We present the most frequent kinds of what are called non-scorable responses along with effective filtering models based on various NLP and speech processing technologies. We give an overview of two operational automated scoring systems {---}one for essay scoring and one for speech scoring{---} and describe the filtering models they use. Finally, we present an evaluation and analysis of filtering models used for spoken responses in an assessment of language proficiency.",
}
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<abstract>In large-scale educational assessments, the use of automated scoring has recently become quite common. While the majority of student responses can be processed and scored without difficulty, there are a small number of responses that have atypical characteristics that make it difficult for an automated scoring system to assign a correct score. We describe a pipeline that detects and processes these kinds of responses at run-time. We present the most frequent kinds of what are called non-scorable responses along with effective filtering models based on various NLP and speech processing technologies. We give an overview of two operational automated scoring systems —one for essay scoring and one for speech scoring— and describe the filtering models they use. Finally, we present an evaluation and analysis of filtering models used for spoken responses in an assessment of language proficiency.</abstract>
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%0 Conference Proceedings
%T Atypical Inputs in Educational Applications
%A Yoon, Su-Youn
%A Cahill, Aoife
%A Loukina, Anastassia
%A Zechner, Klaus
%A Riordan, Brian
%A Madnani, Nitin
%Y Bangalore, Srinivas
%Y Chu-Carroll, Jennifer
%Y Li, Yunyao
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans - Louisiana
%F yoon-etal-2018-atypical
%X In large-scale educational assessments, the use of automated scoring has recently become quite common. While the majority of student responses can be processed and scored without difficulty, there are a small number of responses that have atypical characteristics that make it difficult for an automated scoring system to assign a correct score. We describe a pipeline that detects and processes these kinds of responses at run-time. We present the most frequent kinds of what are called non-scorable responses along with effective filtering models based on various NLP and speech processing technologies. We give an overview of two operational automated scoring systems —one for essay scoring and one for speech scoring— and describe the filtering models they use. Finally, we present an evaluation and analysis of filtering models used for spoken responses in an assessment of language proficiency.
%R 10.18653/v1/N18-3008
%U https://aclanthology.org/N18-3008
%U https://doi.org/10.18653/v1/N18-3008
%P 60-67
Markdown (Informal)
[Atypical Inputs in Educational Applications](https://aclanthology.org/N18-3008) (Yoon et al., NAACL 2018)
ACL
- Su-Youn Yoon, Aoife Cahill, Anastassia Loukina, Klaus Zechner, Brian Riordan, and Nitin Madnani. 2018. Atypical Inputs in Educational Applications. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 60–67, New Orleans - Louisiana. Association for Computational Linguistics.