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LREC-COLING 2024main

MCTS: A Multi-Reference Chinese Text Simplification Dataset

Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

DOI:10.63317/3nqwukjpm34i

Abstract

Text simplification aims to make the text easier to understand by applying rewriting transformations. There has been very little research on Chinese text simplification for a long time. The lack of generic evaluation data is an essential reason for this phenomenon. In this paper, we introduce MCTS, a multi-reference Chinese text simplification dataset. We describe the annotation process of the dataset and provide a detailed analysis. Furthermore, we evaluate the performance of several unsupervised methods and advanced large language models. We additionally provide Chinese text simplification parallel data that can be used for training, acquired by utilizing machine translation and English text simplification. We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research. All of the code and data are released at https://github.com/blcuicall/mcts/.

Details

Paper ID
lrec2024-main-0969
Pages
pp. 11111-11122
BibKey
chong-etal-2024-mcts
Editor
N/A
Publisher
European Language Resources Association (ELRA) and ICCL
ISSN
2522-2686
ISBN
979-10-95546-34-4
Conference
Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Location
Turin, Italy
Date
20 May 2024 25 May 2024

Authors

  • RC

    Ruining Chong

  • LL

    Luming Lu

  • LY

    Liner Yang

  • JN

    Jinran Nie

  • ZL

    Zhenghao Liu

  • SW

    Shuo Wang

  • SZ

    Shuhan Zhou

  • YL

    Yaoxin Li

  • EY

    Erhong Yang

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