Automated Essay Scoring via Pairwise Contrastive Regression

Jiayi Xie, Kaiwei Cai, Li Kong, Junsheng Zhou, Weiguang Qu


Abstract
Automated essay scoring (AES) involves the prediction of a score relating to the writing quality of an essay. Most existing works in AES utilize regression objectives or ranking objectives respectively. However, the two types of methods are highly complementary. To this end, in this paper we take inspiration from contrastive learning and propose a novel unified Neural Pairwise Contrastive Regression (NPCR) model in which both objectives are optimized simultaneously as a single loss. Specifically, we first design a neural pairwise ranking model to guarantee the global ranking order in a large list of essays, and then we further extend this pairwise ranking model to predict the relative scores between an input essay and several reference essays. Additionally, a multi-sample voting strategy is employed for inference. We use Quadratic Weighted Kappa to evaluate our model on the public Automated Student Assessment Prize (ASAP) dataset, and the experimental results demonstrate that NPCR outperforms previous methods by a large margin, achieving the state-of-the-art average performance for the AES task.
Anthology ID:
2022.coling-1.240
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2724–2733
Language:
URL:
https://aclanthology.org/2022.coling-1.240
DOI:
Bibkey:
Cite (ACL):
Jiayi Xie, Kaiwei Cai, Li Kong, Junsheng Zhou, and Weiguang Qu. 2022. Automated Essay Scoring via Pairwise Contrastive Regression. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2724–2733, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Automated Essay Scoring via Pairwise Contrastive Regression (Xie et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.240.pdf