E hi M e NLP at TSAR 2025 Shared Task Candidate Generation via Iterative Simplification and Reranking by Readability and Semantic Similarity (2025)

Abstract: 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.

## introduction text simplification @xcite is a task of paraphrasing complex expressions into simpler ones while preserving the core meaning of a given text. this technology is utilized to support reading comprehension for diverse readers, including children @xcite , language learners @xcite , and individuals with language impairments @xcite . since reading ability varies significantly among individuals, recent studies on text simplification have focused on controlling readability @xcite @xcite @xcite . this paper describes our system submitted to tsar 2025 shared task on readability-controlled text simplification @xcite . our system employs a two-step approach: first, generating candidates for simplified text with large language models (llms), and then reranking the candidates with embeddings. our team was ranked first out of 20 teams in the official evaluation (au-torank). our code is available on github. 1 1 https://github.com/ehimenlp/tsar2025 ## tsar 2025 shared task in this competition @xcite , participants are asked to simplify english paragraphs written at the common european framework of reference for languages (cefr) foot_0 readability level of b2 or more complex into simpler versions at levels b1 and a2, respectively. the cefr is the most widely used international standard for describing the language ability of english learners, consisting of six levels ranging from basic (a1) to proficient (c2). the dataset provided consists of trial data, containing 40 paragraphs, and test data, containing 200 paragraphs. the output texts are automatically evaluated for both rmse of readability and semantic similarity. note that while both bertscore @xcite and meaningbert @xcite are included in the official evaluation script for semantic similarity, only the latter is used in the official final ranking. ## ehimenlp system figure 1 shows an overview of our system. our system employs a two-step strategy of candidate generation and reranking; we describe these proposed methods in sections 3.1 and 3.2. we then provide implementation details in section 3.3. finally, we report the results of preliminary experiments on the trial dataset in section 3.4. ## evaluation our ehimenlp system achieved first place in the official ranking @xcite . as shown in ## conclusion we described the ehimenlp submission for the tsar 2025 shared task. our system employed a two-step strategy in which llms generated diverse candidates, followed by re-ranking based on readability and semantic similarity, achieving first place among 20 teams in the official ranking.