Papers › A Neural Pairwise Ranking Model for Readability Assessment

A Neural Pairwise Ranking Model for Readability Assessment

14 Mar 2022Findings (ACL) 2022 5arXiv:2203.07450archive 2025-07-28

Justin Lee, Sowmya Vajjala

Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research. In this paper, we propose the first neural, pairwise ranking approach to ARA and compare it with existing classification, regression, and (non-neural) ranking methods. We establish the performance of our model by conducting experiments with three English, one French and one Spanish datasets. We demonstrate that our approach performs well in monolingual single/cross corpus testing scenarios and achieves a zero-shot cross-lingual ranking accuracy of over 80% for both French and Spanish when trained on English data. Additionally, we also release a new parallel bilingual readability dataset in English and French. To our knowledge, this paper proposes the first neural pairwise ranking model for ARA, and shows the first results of cross-lingual, zero-shot evaluation of ARA with neural models.

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Text Classification OneStopEnglish (Readability Assessment) NPRM-BERT Accuracy (5-fold) 0.979 #2 of 5 Archive leaderboard report

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