Multi-task Learning for Automated Essay Scoring with Sentiment Analysis

Panitan Muangkammuen, Fumiyo Fukumoto

Abstract
Automated Essay Scoring (AES) is a process that aims to alleviate the workload of graders and improve the feedback cycle in educational systems. Multi-task learning models, one of the deep learning techniques that have recently been applied to many NLP tasks, demonstrate the vast potential for AES. In this work, we present an approach for combining two tasks, sentiment analysis, and AES by utilizing multi-task learning. The model is based on a hierarchical neural network that learns to predict a holistic score at the document-level along with sentiment classes at the word-level and sentence-level. The sentiment features extracted from opinion expressions can enhance a vanilla holistic essay scoring, which mainly focuses on lexicon and text semantics. Our approach demonstrates that sentiment features are beneficial for some essay prompts, and the performance is competitive to other deep learning models on the Automated StudentAssessment Prize (ASAP) benchmark. TheQuadratic Weighted Kappa (QWK) is used to measure the agreement between the human grader’s score and the model’s prediction. Ourmodel produces a QWK of 0.763.
Anthology ID:
2020.aacl-srw.17
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
AACL
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Publisher:
Association for Computational Linguistics
Note:
Pages:
116–123
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URL:
https://aclanthology.org/2020.aacl-srw.17
DOI:
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Cite (ACL):
Panitan Muangkammuen and Fumiyo Fukumoto. 2020. Multi-task Learning for Automated Essay Scoring with Sentiment Analysis. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 116–123, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis (Muangkammuen & Fukumoto, AACL 2020)
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