We train a relative sentence readability estimator from a corpus without absolute sentence readability.Since sentence readability depends on the reader’s knowledge, objective and absolute readability assessments require costly annotation by experts.Therefore, few corpora have absolute sentence readability, while parallel corpora for text simplification with relative sentence readability between two sentences are available for many languages.With multilingual applications in mind, we propose a method to estimate relative sentence readability based on parallel corpora for text simplification.Experimental results on ranking a set of English sentences by readability show that our method outperforms existing unsupervised methods and is comparable to supervised methods based on absolute sentence readability.

We introduce the EhiMeNLP submission, which won the TSAR 2025 Shared Task on Readability-Controlled Text Simplification. Our system employed a two-step strategy of candidate generation and reranking. For candidate generation, we simplified the given text into more readable versions by combining multiple large language models with prompts. Then, for reranking, we selected the best candidate by readability-based filtering and ranking based on semantic similarity to the original text.