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        <title>Essay Quality Signals as Weak Supervision for Source-based Essay Scoring</title>
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    <abstract>Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs.</abstract>
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%0 Conference Proceedings
%T Essay Quality Signals as Weak Supervision for Source-based Essay Scoring
%A Zhang, Haoran
%A Litman, Diane
%S Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F zhang-litman-2021-essay
%X Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES model. Unfortunately, such a graded corpus often does not exist, so creating a corpus for machine learning can also be a laborious task. This paper presents an investigation of replacing the use of human-labeled essay grades when training an AES system with two automatically available but weaker signals of essay quality: word count and topic distribution similarity. Experiments using two source-based essay scoring (evidence score) corpora show that while weak supervision does not yield a competitive result when training a neural source-based AES model, it can be used to successfully extract Topical Components (TCs) from a source text, which are required by a supervised feature-based AES model. In particular, results show that feature-based AES performance is comparable with either automatically or manually constructed TCs.
%U https://aclanthology.org/2021.bea-1.9
%P 85-96

Markdown (Informal)

[Essay Quality Signals as Weak Supervision for Source-based Essay Scoring](https://aclanthology.org/2021.bea-1.9) (Zhang & Litman, BEA 2021)

• Essay Quality Signals as Weak Supervision for Source-based Essay Scoring (Zhang & Litman, BEA 2021)

ACL

• Haoran Zhang and Diane Litman. 2021. Essay Quality Signals as Weak Supervision for Source-based Essay Scoring. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 85–96, Online. Association for Computational Linguistics.