@inproceedings{wang-etal-2019-inject,
title = "Inject Rubrics into Short Answer Grading System",
author = "Wang, Tianqi and
Inoue, Naoya and
Ouchi, Hiroki and
Mizumoto, Tomoya and
Inui, Kentaro",
editor = "Cherry, Colin and
Durrett, Greg and
Foster, George and
Haffari, Reza and
Khadivi, Shahram and
Peng, Nanyun and
Ren, Xiang and
Swayamdipta, Swabha",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6119",
doi = "10.18653/v1/D19-6119",
pages = "175--182",
abstract = "Short Answer Grading (SAG) is a task of scoring students{'} answers in examinations. Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance. But they ignore important evaluation criteria such as rubrics, which play a crucial role for evaluating answers in real-world situations. In this paper, we present a method to inject information from rubrics into SAG systems. We implement our approach on top of word-level attention mechanism to introduce the rubric information, in order to locate information in each answer that are highly related to the score. Our experimental results demonstrate that injecting rubric information effectively contributes to the performance improvement and that our proposed model outperforms the state-of-the-art SAG model on the widely used ASAP-SAS dataset under low-resource settings.",
}
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<abstract>Short Answer Grading (SAG) is a task of scoring students’ answers in examinations. Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance. But they ignore important evaluation criteria such as rubrics, which play a crucial role for evaluating answers in real-world situations. In this paper, we present a method to inject information from rubrics into SAG systems. We implement our approach on top of word-level attention mechanism to introduce the rubric information, in order to locate information in each answer that are highly related to the score. Our experimental results demonstrate that injecting rubric information effectively contributes to the performance improvement and that our proposed model outperforms the state-of-the-art SAG model on the widely used ASAP-SAS dataset under low-resource settings.</abstract>
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%0 Conference Proceedings
%T Inject Rubrics into Short Answer Grading System
%A Wang, Tianqi
%A Inoue, Naoya
%A Ouchi, Hiroki
%A Mizumoto, Tomoya
%A Inui, Kentaro
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F wang-etal-2019-inject
%X Short Answer Grading (SAG) is a task of scoring students’ answers in examinations. Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance. But they ignore important evaluation criteria such as rubrics, which play a crucial role for evaluating answers in real-world situations. In this paper, we present a method to inject information from rubrics into SAG systems. We implement our approach on top of word-level attention mechanism to introduce the rubric information, in order to locate information in each answer that are highly related to the score. Our experimental results demonstrate that injecting rubric information effectively contributes to the performance improvement and that our proposed model outperforms the state-of-the-art SAG model on the widely used ASAP-SAS dataset under low-resource settings.
%R 10.18653/v1/D19-6119
%U https://aclanthology.org/D19-6119
%U https://doi.org/10.18653/v1/D19-6119
%P 175-182
Markdown (Informal)
[Inject Rubrics into Short Answer Grading System](https://aclanthology.org/D19-6119) (Wang et al., 2019)
ACL
- Tianqi Wang, Naoya Inoue, Hiroki Ouchi, Tomoya Mizumoto, and Kentaro Inui. 2019. Inject Rubrics into Short Answer Grading System. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 175–182, Hong Kong, China. Association for Computational Linguistics.