A Neural Pairwise Ranking Model for Readability Assessment
Justin Lee, Sowmya Vajjala · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
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, zeroshot evaluation of ARA with neural models.