# Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese

arXiv: https://arxiv.org/abs/2406.02974 · HTML: https://mlsanity.com/paper/2406.02974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing](https://mlsanity.com/paper/1906.08104/) (2019)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [EASSE: Easier Automatic Sentence Simplification Evaluation](https://mlsanity.com/paper/1908.04567/) (2019)
- [Supervised and Unsupervised Neural Approaches to Text Readability](https://mlsanity.com/paper/1907.11779/) (2019)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [Semantic Structural Evaluation for Text Simplification](https://mlsanity.com/paper/1810.05022/) (2018)
- [Revisiting non-English Text Simplification: A Unified Multilingual Benchmark](https://mlsanity.com/paper/2305.15678/) (2023)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models](https://mlsanity.com/paper/2308.13961/) (2023)
- [Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification](https://mlsanity.com/paper/1904.02767/) (2019)
- [Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders](https://mlsanity.com/paper/2004.14693/) (2020)
- [An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages](https://mlsanity.com/paper/2109.00165/) (2021)
- [Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification](https://mlsanity.com/paper/2310.08170/) (2023)
- [Learning to Paraphrase Sentences to Different Complexity Levels](https://mlsanity.com/paper/2308.02226/) (2023)
- [Reference-less Quality Estimation of Text Simplification Systems](https://mlsanity.com/paper/1901.10746/) (2019)
- [Exploiting Summarization Data to Help Text Simplification](https://mlsanity.com/paper/2302.07124/) (2023)
- [A New Dataset and Empirical Study for Sentence Simplification in Chinese](https://mlsanity.com/paper/2306.04188/) (2023)
- [Multilingual Lexical Simplification via Paraphrase Generation](https://mlsanity.com/paper/2307.15286/) (2023)

## Built on by (most cited first, 1 of 1)

- [Readability Measures and Automatic Text Simplification: In the Search of a Construct](https://mlsanity.com/paper/2511.09536/) (2025)

## Abstract (arXiv)

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques with lexcial simplification. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification.