Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation

Jingshen Zhang; Xiangyu Yang; Xinkai Su; Xinglu Chen; Tianyou Huang; Xin Ying Qiu

Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation

Jingshen Zhang, Xiangyu Yang, Xinkai Su, Xinglu Chen, Tianyou Huang, Xinying Qiu

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Abstract

“This system report presents our approaches and results for the Chinese Essay Fluency Evaluation (CEFE) task at CCL-2024. For Track 1, we optimized predictions for challenging fine-grained error types using binary classification models and trained coarse-grained models on the Chinese Learner 4W corpus. In Track 2, we enhanced performance by constructing a pseudo-dataset with multiple error types per sentence. For Track 3, where we achieved first place, we generated fluency-rated pseudo-data via back-translation for pretraining and used an NSP-based strategy with Symmetric Cross Entropy loss to capture context and mitigate long dependencies. Our methods effectively address key challenges in Chinese Essay Fluency Evaluation.”

Anthology ID:: 2024.ccl-3.30
Volume:: Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
Month:: July
Year:: 2024
Address:: Taiyuan, China
Editors:: Lin Hongfei, Tan Hongye, Li Bin
Venue:: CCL
SIG:
Publisher:: Chinese Information Processing Society of China
Note:
Pages:: 269–277
Language:: English
URL:: https://aclanthology.org/2024.ccl-3.30/
DOI:
Bibkey:
Cite (ACL):: Jingshen Zhang, Xiangyu Yang, Xinkai Su, Xinglu Chen, Tianyou Huang, and Xinying Qiu. 2024. Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 269–277, Taiyuan, China. Chinese Information Processing Society of China.
Cite (Informal):: Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation (Zhang et al., CCL 2024)
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PDF:: https://aclanthology.org/2024.ccl-3.30.pdf

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@inproceedings{jingshen-etal-2024-multi,
    title = "Multi-Error Modeling and Fluency-Targeted Pre-training for {C}hinese Essay Evaluation",
    author = "Zhang, Jingshen  and
      Yang, Xiangyu  and
      Su, Xinkai  and
      Chen, Xinglu  and
      Huang, Tianyou  and
      Qiu, Xinying",
    editor = "Hongfei, Lin  and
      Hongye, Tan  and
      Bin, Li",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://aclanthology.org/2024.ccl-3.30/",
    pages = "269--277",
    language = "eng",
    abstract = "``This system report presents our approaches and results for the Chinese Essay Fluency Evaluation (CEFE) task at CCL-2024. For Track 1, we optimized predictions for challenging fine-grained error types using binary classification models and trained coarse-grained models on the Chinese Learner 4W corpus. In Track 2, we enhanced performance by constructing a pseudo-dataset with multiple error types per sentence. For Track 3, where we achieved first place, we generated fluency-rated pseudo-data via back-translation for pretraining and used an NSP-based strategy with Symmetric Cross Entropy loss to capture context and mitigate long dependencies. Our methods effectively address key challenges in Chinese Essay Fluency Evaluation.''"
}

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%0 Conference Proceedings
%T Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation
%A Zhang, Jingshen
%A Yang, Xiangyu
%A Su, Xinkai
%A Chen, Xinglu
%A Huang, Tianyou
%A Qiu, Xinying
%Y Hongfei, Lin
%Y Hongye, Tan
%Y Bin, Li
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F jingshen-etal-2024-multi
%X “This system report presents our approaches and results for the Chinese Essay Fluency Evaluation (CEFE) task at CCL-2024. For Track 1, we optimized predictions for challenging fine-grained error types using binary classification models and trained coarse-grained models on the Chinese Learner 4W corpus. In Track 2, we enhanced performance by constructing a pseudo-dataset with multiple error types per sentence. For Track 3, where we achieved first place, we generated fluency-rated pseudo-data via back-translation for pretraining and used an NSP-based strategy with Symmetric Cross Entropy loss to capture context and mitigate long dependencies. Our methods effectively address key challenges in Chinese Essay Fluency Evaluation.”
%U https://aclanthology.org/2024.ccl-3.30/
%P 269-277

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Markdown (Informal)

[Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation](https://aclanthology.org/2024.ccl-3.30/) (Zhang et al., CCL 2024)

Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation (Zhang et al., CCL 2024)

ACL

Jingshen Zhang, Xiangyu Yang, Xinkai Su, Xinglu Chen, Tianyou Huang, and Xinying Qiu. 2024. Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations), pages 269–277, Taiyuan, China. Chinese Information Processing Society of China.