Aligning Sentence Simplification with ESL Learner’s Proficiency for Language Acquisition (2025)

Abstract: Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. This study goes a step further and aims to facilitate ESL learners’ language acquisition by simplification. Specifically, we propose simplifying complex sentences to appropriate levels for learners while also increasing vocabulary coverage of the target level in the simplifications. We achieve this without a parallel corpus by conducting reinforcement learning on a large language model. Our method employs token-level and sentence-level rewards, and iteratively trains the model on its self-generated outputs to guide the model to search for simplification hypotheses that satisfy the target attributes. Experiment results on CEFR-SP and TurkCorpus datasets show that the proposed method can effectively increase the frequency and diversity of vocabulary of the target level by more than 20% compared to baseline models, while maintaining high simplification quality.

## introduction controlled text simplification considers audiencetargeted attributes when generating simplified texts, so that the generated texts do not only meet the criteria of simplicity, but also preserve desired attributes for the targeted audiences. recent studies on controlled text simplification aimed to help reading comprehension for language learners and employed school grade levels annotated in the training corpus as the simplification target @xcite @xcite or text features (number of words, character-level levenshtein similarity etc.) between source and target sentences @xcite . different from these studies, we aim to aid language learning and education for english as a sec-ond language (esl) learners by simplifying sentences while preserving desirable attributes for language acquisition. we use the common european framework of reference for languages (cefr), the world standard definition of language proficiency. our method is motivated by two classic l2 learning theories: the input hypothesis @xcite and frequency effect @xcite . the input hypothesis stated that in order for the language acquisition to happen, the textual input which is either too simple or too complex for learner comprehension will not be useful for acquisition. if a learner's current competence is i, then comprehensible input should contain both i and (i+1) content @xcite . frequency theory holds that the frequency of the words and phrases in the input is a key determinant of acquisition, and words with higher frequency in the usage tend to be easier to acquire @xcite . the key challenge here is the lack of a parallel corpus for training that should provide complex-simple sentence pairs labelled their levels. parallel sentences of this kind are naturally scarce, and worse, annotation of difficulty levels, in particular, cefr, is non-trivial and requires language education experts @xcite . to achieve sentence simplification for aiding language learning without a parallel corpus, we propose reinforcement learning on a pre-trained large language model (llm). based on the aforementioned l2 learning theories, the proposed method simplifies the complex sentences to the one corresponding to the learner's proficiency level or one level higher (i and i + 1 levels) and increases the coverage (frequency and diversity) of the corresponding level's vocabulary in the generated simplifications. specifically, we reformulate the controlled simplification task as a lookahead search problem: in the decoding step t, the model searches for the token that satisfies the target vocabulary constraint while also ensuring that future tokens increase the target vocabulary coverage as much as possible, and the final hypothesis falls into the desired cefr level. we combine a simple wordmatch-based heuristic with the supervised sentencelevel signal to guide decoding and train the model iteratively using gradient policy optimization to memorize the search strategy that maximizes the overall reward. remarkably, we eliminate the need for a parallel corpus by utilizing llms' language generation capacity for simplification via reinforcement learning. experimental results show that the method significantly increases the coverage and diversity of the target vocabulary in the outputs by up to 20% compared to the baselines, while maintaining high simplification quality. our primary contributions are twofold. first, we propose the sentence simplification method that aligns generated simplifications with esl learners' proficiency level on word, phrase and sentence levels and preserves attributes effective for facilitating language learning. second, our method is easy to deploy and does not require a parallel corpus that is often expensive to create. ## related work we briefly summarize two lines of simplification methods, controlled simplification and reinforcement learning based simplification. controlled simplification attaches tokens or prompts to the input to control the simplificationrelated attributes during generation @xcite @xcite @xcite @xcite . while these methods learn to control levels of simplified sentences using a parallel corpus with annotated difficulties, our method controls attributes useful for language learning without a parallel corpus. as opposed to the training-time controlling, kew and ebling (2022) adopted fudge @xcite , which adjusts the logits of the text generation model during decoding using a classifier, to directly control the attribute of the simplification in the decoding time. reinforcement learning based simplification has explored controllability by defining rewards based on simplicity-related criteria @xcite @xcite . the rewards for the objectives are constructed using supervised or unsupervised evaluation metrics for simplicity, adequacy and fluency. in contrast, we aim to control attributes useful for language learning and education. furthermore, while rl tends to suffer from unstable training and sensitivity to the choice of hyperparameters, our method achieves training stability by adopting entropy regularization in the model optimization process and introducing a dynamic reward that adjusts based on the data distribution. ## problem definition we aim to facilitate language learning by simplification targeted at esl learners. in this study, we use cefr levels as a representative measure for the learners' proficiency and model the target level based on the vocabulary foot_1 (words, phrases, idioms) and sentence cefr levels foot_2 . our problem is thus defined as follows. we assume that learners know their own cefr level i. given a sentence above the learner's level i, we generate its simplified version that (a) contains as much vocabulary of the level i and i+1 as possible, and (b) corresponds to the target (learner's) level i at the sentence level. ## proposed method to search for a hypothesis that better satisfies predetermined constraints, some previous methods use rollout in decoding that generates partial future sequences @xcite . these methods become infeasible for large models due to the inefficiency of sampling in decoding time and handling the large vocabulary constraints in our task. to effectively and efficiently search for the tokens that satisfy our constraints, we instead consider sampling in the training time and formulate the lookahead search problem using rl search @xcite (see fig. 1 ). ## experiment settings we aim to evaluate the effectiveness of the proposed model in generating high-quality simplifications that align with the target vocabulary and sentence-based cefr level. this section provides details of the experiment settings. ## experiment results this section analyses experiment results of automatic and human evaluations, and ablation study. ## conclusion in this paper, we target esl learners as audiences for text simplification aiming to facilitate the learning process of the foreign language. referring to the input hypothesis and frequency effect theory in l2 learning, we propose a reinforcement learning method on llm to control the simplification model to generate outputs that satisfy the vocabulary and sentence level constraints. experiment results show that the proposed method can increase the target vocabulary coverage in the generated simplifications, and human evaluation results confirmed that the simplified texts generally preserve the targeted cefr levels. in practice, different individuals have varied lev-els of knowledge for the language. we plan to extend the method to generate individual learnertargeted personalized simplifications in the future.