@inproceedings{song-etal-2020-multi,
title = "Multi-Stage Pre-training for Automated {C}hinese Essay Scoring",
author = "Song, Wei and
Zhang, Kai and
Fu, Ruiji and
Liu, Lizhen and
Liu, Ting and
Cheng, Miaomiao",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.546",
doi = "10.18653/v1/2020.emnlp-main.546",
pages = "6723--6733",
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..",
}
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%0 Conference Proceedings
%T Multi-Stage Pre-training for Automated Chinese Essay Scoring
%A Song, Wei
%A Zhang, Kai
%A Fu, Ruiji
%A Liu, Lizhen
%A Liu, Ting
%A Cheng, Miaomiao
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F song-etal-2020-multi
%X 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..
%R 10.18653/v1/2020.emnlp-main.546
%U https://aclanthology.org/2020.emnlp-main.546
%U https://doi.org/10.18653/v1/2020.emnlp-main.546
%P 6723-6733
Markdown (Informal)
[Multi-Stage Pre-training for Automated Chinese Essay Scoring](https://aclanthology.org/2020.emnlp-main.546) (Song et al., EMNLP 2020)
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.