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        <title>Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.</abstract>
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        <date>2020-07</date>
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            <start>8569</start>
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%0 Conference Proceedings
%T Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring
%A Zhang, Haoran
%A Litman, Diane
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhang-litman-2020-automated
%X While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.
%R 10.18653/v1/2020.acl-main.759
%U https://aclanthology.org/2020.acl-main.759
%U https://doi.org/10.18653/v1/2020.acl-main.759
%P 8569-8584

Markdown (Informal)

[Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring](https://aclanthology.org/2020.acl-main.759) (Zhang & Litman, ACL 2020)

• Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring (Zhang & Litman, ACL 2020)

ACL

• Haoran Zhang and Diane Litman. 2020. Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8569–8584, Online. Association for Computational Linguistics.