@inproceedings{mizumoto-etal-2019-analytic,
title = "Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring",
author = "Mizumoto, Tomoya and
Ouchi, Hiroki and
Isobe, Yoriko and
Reisert, Paul and
Nagata, Ryo and
Sekine, Satoshi and
Inui, Kentaro",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4433",
doi = "10.18653/v1/W19-4433",
pages = "316--325",
abstract = "This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts. We first propose and formalize two novel analytical assessment tasks: analytic score prediction and justification identification, and then provide the first dataset created for analytic short answer scoring research. Subsequently, we present a neural baseline model and report our extensive empirical results to demonstrate how our dataset can be used to explore new and intriguing technical challenges in short answer scoring. The dataset is publicly available for research purposes.",
}
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%0 Conference Proceedings
%T Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring
%A Mizumoto, Tomoya
%A Ouchi, Hiroki
%A Isobe, Yoriko
%A Reisert, Paul
%A Nagata, Ryo
%A Sekine, Satoshi
%A Inui, Kentaro
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F mizumoto-etal-2019-analytic
%X This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts. We first propose and formalize two novel analytical assessment tasks: analytic score prediction and justification identification, and then provide the first dataset created for analytic short answer scoring research. Subsequently, we present a neural baseline model and report our extensive empirical results to demonstrate how our dataset can be used to explore new and intriguing technical challenges in short answer scoring. The dataset is publicly available for research purposes.
%R 10.18653/v1/W19-4433
%U https://aclanthology.org/W19-4433
%U https://doi.org/10.18653/v1/W19-4433
%P 316-325
Markdown (Informal)
[Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring](https://aclanthology.org/W19-4433) (Mizumoto et al., BEA 2019)
ACL