@inproceedings{zhang-litman-2020-automated,
title = "Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring",
author = "Zhang, Haoran and
Litman, Diane",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.759",
doi = "10.18653/v1/2020.acl-main.759",
pages = "8569--8584",
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.",
}
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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)
ACL