Multi-Stage Pre-training for Automated Chinese Essay Scoring

Wei Song, Kai Zhang, Ruiji Fu, Lizhen Liu, Ting Liu, Miaomiao Cheng


Abstract
This paper proposes a pre-training based automated Chinese essay scoring method. The method involves three components: weakly supervised pre-training, supervised cross- prompt fine-tuning and supervised target- prompt fine-tuning. An essay scorer is first pre- trained on a large essay dataset covering diverse topics and with coarse ratings, i.e., good and poor, which are used as a kind of weak supervision. The pre-trained essay scorer would be further fine-tuned on previously rated es- says from existing prompts, which have the same score range with the target prompt and provide extra supervision. At last, the scorer is fine-tuned on the target-prompt training data. The evaluation on four prompts shows that this method can improve a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations..
Anthology ID:
2020.emnlp-main.546
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6723–6733
Language:
URL:
https://aclanthology.org/2020.emnlp-main.546
DOI:
10.18653/v1/2020.emnlp-main.546
Bibkey:
Cite (ACL):
Wei Song, Kai Zhang, Ruiji Fu, Lizhen Liu, Ting Liu, and Miaomiao Cheng. 2020. Multi-Stage Pre-training for Automated Chinese Essay Scoring. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6723–6733, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-Stage Pre-training for Automated Chinese Essay Scoring (Song et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.546.pdf
Video:
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