MCTS: A Multi-Reference Chinese Text Simplification Dataset
This work addresses a gap in Chinese text simplification research by providing a foundational dataset, though it is incremental as it adapts existing methods to a new language domain.
The authors tackled the lack of evaluation data for Chinese text simplification by introducing MCTS, a multi-reference dataset, and found that advanced large language models performed well in evaluations.
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/.