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        <title>A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading</title>
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        <namePart type="given">Shourya</namePart>
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            <namePart type="given">Hsin-Hsi</namePart>
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            <namePart type="given">Yuen-Hsien</namePart>
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    <abstract>We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smoothing approach, based on classical sequential pattern mining, exploits lexical overlap in students’ answers to any typical question. We empirically demonstrate using multiple datasets that the proposed approach improves the overall performance and significantly reduces (up to 63%) variation in performance (standard deviation) of unsupervised ASAG techniques. We bring in additional benchmarks such as (a) paraphrasing of model answers and (b) using answers by k top performing students as model answers, to amplify the benefits of the proposed approach.</abstract>
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%0 Conference Proceedings
%T A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading
%A Roy, Shourya
%A Dandapat, Sandipan
%A Narahari, Y.
%Y Chen, Hsin-Hsi
%Y Tseng, Yuen-Hsien
%Y Ng, Vincent
%Y Lu, Xiaofei
%S Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F roy-etal-2016-fluctuation
%X We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smoothing approach, based on classical sequential pattern mining, exploits lexical overlap in students’ answers to any typical question. We empirically demonstrate using multiple datasets that the proposed approach improves the overall performance and significantly reduces (up to 63%) variation in performance (standard deviation) of unsupervised ASAG techniques. We bring in additional benchmarks such as (a) paraphrasing of model answers and (b) using answers by k top performing students as model answers, to amplify the benefits of the proposed approach.
%U https://aclanthology.org/W16-4911
%P 82-91

Markdown (Informal)

[A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading](https://aclanthology.org/W16-4911) (Roy et al., NLP-TEA 2016)

• A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading (Roy et al., NLP-TEA 2016)

ACL

• Shourya Roy, Sandipan Dandapat, and Y. Narahari. 2016. A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 82–91, Osaka, Japan. The COLING 2016 Organizing Committee.