Readability-guided Idiom-aware Sentence Simplification ( RISS ) for C hinese (2024)

Abstract: “Chinese sentence simplification faces challenges due to the lack of large-scale labeledparallel corpora and the prevalence of idioms. To address these challenges, we pro-pose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel frameworkthat combines data augmentation techniques. RISS introduces two key components: (1)Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sen-tence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the compre-hension and simplification of idiomatic expressions. By integrating RPS and IAS usingmulti-stage and multi-task learning strategies, RISS outperforms previous state-of-the-artmethods on two Chinese sentence simplification datasets. Furthermore, RISS achievesadditional improvements when fine-tuned on a small labeled dataset. Our approachdemonstrates the potential for more effective and accessible Chinese text simplification.”

## introduction sentence simplification (ss) aims to reduce the linguistic complexity of a sentence while preserving its meaning @xcite . it has various societal applications, such as increasing accessibility for people with cognitive disabilities @xcite and second language learners @xcite . while english sentence simplification has made significant progress due to the availability of large-scale parallel corpora, other languages face the challenge of limited publicly available resources @xcite . for chinese, in particular, the publicly accessible sentence simplification corpora contain only around 5,000 entries @xcite . this scarcity of labeled data is a primary hurdle for advancing chinese sentence simplification research. in addition to the lack of parallel corpora, chinese sentence simplification faces another challenge: the prevalence of idioms. idioms are non-compositional expressions whose metaphorical meanings deviate from the literal meanings of their constituent words @xcite . in chinese, idioms are frequently used in sentences, and their simplification often requires providing a brief explanatory sentence rather than simply replacing them with synonyms, as is common in traditional lexical simplification. to address these challenges, we propose readability-guided idiom-aware sentence simplification (riss), a novel framework combining data augmentation and transfer learning to address the challenges in chinese sentence simplification. riss integrates readability-guided paraphrase selection (rps), which mines high-quality sentence pairs from chinese paraphrases, and idiom-aware simplification (ias), an idiom-based transfer learning strategy. through multistage and multi-task learning, riss outperforms state-of-the-art methods on two datasets without labeled data and further improves when fine-tuned on a small labeled dataset. the paper is organized as follows: section 2 reviews related work, section 3 describes our methodologies (rps, ias, and riss), section 4 outlines the experimental setup, section 5 presents and analyzes the results, and section 6 provides conclusions and future directions. the code, data and outputs of the proposed method are available at https: //github.com/astro-jon/riss 2 related work ## methodology our proposed readability-guided idiom-aware sentence simplification framework combines two novel methodologies: rps for mining high-quality paraphrase data and ias for idiomatic transfer learning. we integrate these modules using multi-stage and multi-task learning strate-c c l 2 0 2 4 gies to enhance the performance of sentence simplification models. we provide a glossary of abbreviations in appendix a. ## experiment design in this section, we present the experimental setup for evaluating the proposed riss framework. we describe the datasets used for training and evaluation, the evaluation metrics, and the baseline methods compared against riss. and we train all the models using implementation of bart-base-chinese @xcite . ## results and analysis in this section, we present the experimental results of the proposed readability-guided idiom-aware sentence simplification (riss) framework and compare its performance against the baseline methods on the css and mcts datasets. we also conduct an ablation study to investigate the individual contributions of the rps and ias modules. ## conclusion in this work, we propose readability-guided idiom-aware sentence simplification (riss), a novel framework that addresses two major challenges in chinese sentence simplification: the scarcity of labeled parallel corpora and the prevalence of idioms. riss combines two key components: readability-guided paraphrase selection (rps), which mines high-quality paraphrase pairs from a large corpus based on readability differences and linguistic similarities, and idiomaware simplification (ias), an idiom paraphrasing model that leverages an idiom-specific loss function to enhance the comprehension and simplification of idiomatic expressions. by integrating rps and ias through multi-stage and multi-task learning strategies, riss achieves state-of-the-art performance on two chinese sentence simplification datasets, even without using labeled simplification data. furthermore, when fine-tuned on a small labeled dataset, riss demonstrates further improvements, advancing the state-of-the-art in chinese sentence simplification. we hope that our work will inspire future research in low-resource sentence simplification and contribute to making written content more accessible to diverse audiences.