CoNST: Context-Aware Neural Method for Word Replacement and Sentence Simplification to Improve Text Accessibility
Why this work is in the frame
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Bibliographic record
Abstract
A primary task in Natural Language Processing (NLP) is text simplification, which aims to create text that is more comprehensible and accessible to a broader audience. Lowering linguistic difficulty and restructuring sentences enhances readability, making it easier for individuals with varying literacy levels and skills. However, many unsupervised lexical simplification methods currently in use primarily focus on individual complex words without considering the surrounding context, which often results in inappropriate substitutions. In this work, we propose a neural-based sentence simplification approach called CoNST and compare it against a rule-based method and other baseline systems, and operate in three sequential stages. In the CoNST neural-based approach, complex word identification is performed using a Bi-LSTM sequential architecture, followed by substitute generation with a BERT model pre-trained for Masked Language Modeling (MLM). Finally, alternatives are ranked to deliver the simplest possible sentence. In contrast, the experimental results show that the neural-based approach achieves a SARI score of 40.24, outperforming the rule-based technique and other baselines.These results demonstrate the advantages of combining advanced neural models for context-aware word replacement with a structured evaluation model. In an era of information overload and diverse literacy needs, this work contributes a robust and effective method for improving linguistic clarity and accessibility.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it