A New Dataset and Empirical Study for Sentence Simplification in C hinese (2023)

Abstract: Sentence Simplification is a valuable technique that can benefit language learners and children a lot. However, current research focuses more on English sentence simplification. The development of Chinese sentence simplification is relatively slow due to the lack of data. To alleviate this limitation, this paper introduces CSS, a new dataset for assessing sentence simplification in Chinese. We collect manual simplifications from human annotators and perform data analysis to show the difference between English and Chinese sentence simplifications. Furthermore, we test several unsupervised and zero/few-shot learning methods on CSS and analyze the automatic evaluation and human evaluation results. In the end, we explore whether Large Language Models can serve as high-quality Chinese sentence simplification systems by evaluating them on CSS.

## introduction sentence simplification (ss) is the task of modifying a sentence to make it easier to understand and improve its accessibility for a wider audience, while retaining most of its original meaning @xcite . automatic ss systems provide reading assistance to children @xcite , nonnative readers @xcite , and people with reading disabilities @xcite @xcite . currently, there are multiple datasets for english sentence simplification to choose from, such as wikismall @xcite , wikilarge @xcite and newsela @xcite . however, few simplification-specific datasets are available for other languages. research on other popular languages like spanish @xcite , french @xcite ), ## css in this section, we describe detailed information about our css dataset. specifically, we first give the annotation process of the css dataset in section 3.1. then, we show statistical information about our css dataset in section 3.2. in sections 3.3 and 3.4, we do automatic and manual data analysis on css. and an additional dataset for few-shot setting is described in section 3.5. ## experiments in this section, we conducted a series of experiments to explore how to train chinese ss models in low-resource scenarios. some of them could serve as the baselines for future studies. all the models are tested on css. following @xcite , we first implement the following simple methods for comparison. identity it simply outputs the original sentence, which means the original sentence and the simplification are exactly the same. truncation the original sentence is truncated and only the first 80 percent of words are retained. ## conclusion in this paper, we are committed to facilitating research on chinese sentence simplification. we introduced css, a new dataset for the evaluation of chinese ss models. simplifications in css were manually written by human annotators, and the simplification operations are also labeled. we give an in-depth analysis of css and reveal its characteristics. we further develop several unsupervised and zero/few-short methods on the dataset, and the experiment results can benefit future research.