@inproceedings{jingshen-etal-2024-multi,
title = "Multi-Error Modeling and Fluency-Targeted Pre-training for {C}hinese Essay Evaluation",
author = "Jingshen, Zhang and
Xiangyu, Yang and
Xinkai, Su and
Xinglu, Chen and
Tianyou, Huang and
Xinying, Qiu",
editor = "Lin, Hongfei and
Tan, Hongye and
Li, Bin",
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 = "{\textquotedblleft}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.{\textquotedblright}"
}
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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.”</abstract>
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%0 Conference Proceedings
%T Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation
%A Jingshen, Zhang
%A Xiangyu, Yang
%A Xinkai, Su
%A Xinglu, Chen
%A Tianyou, Huang
%A Xinying, Qiu
%Y Lin, Hongfei
%Y Tan, Hongye
%Y Li, Bin
%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
Markdown (Informal)
[Multi-Error Modeling and Fluency-Targeted Pre-training for Chinese Essay Evaluation](https://aclanthology.org/2024.ccl-3.30/) (Jingshen et al., CCL 2024)
ACL