Controlling text difficulty is crucial for adapting texts to individual language learners. Existing controllable text simplification methods condition generation on discrete proficiency-level tokens, treating these levels as independent categories. As a result, they fail to capture the ordinal structure of language proficiency and cannot provide fine-grained control beyond predefined levels. To address this limitation, we propose a continuous difficulty control method that adjusts simplification difficulty by editing the norm of a conditioning embedding derived from the input sentence semantics. The embedding is incorporated into the transformer decoder through attention-based conditioning, enabling continuous control over output difficulty while modeling ordinal relationships across levels. Unlike token-based approaches, our method supports interpolation between proficiency levels and flexible control over edit magnitude. Experiments on controllable text simplification benchmarks demonstrate improved difficulty controllability, simplification quality, and sentence difficulty estimation performance.

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.

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.