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        <title>A Neural Pairwise Ranking Model for Readability Assessment</title>
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    <abstract>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 approach 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, that could be useful for future research. 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.</abstract>
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%0 Conference Proceedings
%T A Neural Pairwise Ranking Model for Readability Assessment
%A Lee, Justin
%A Vajjala, Sowmya
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lee-vajjala-2022-neural
%X 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 approach 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, that could be useful for future research. 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.
%R 10.18653/v1/2022.findings-acl.300
%U https://aclanthology.org/2022.findings-acl.300
%U https://doi.org/10.18653/v1/2022.findings-acl.300
%P 3802-3813

Markdown (Informal)

[A Neural Pairwise Ranking Model for Readability Assessment](https://aclanthology.org/2022.findings-acl.300) (Lee & Vajjala, Findings 2022)

• A Neural Pairwise Ranking Model for Readability Assessment (Lee & Vajjala, Findings 2022)

ACL

• Justin Lee and Sowmya Vajjala. 2022. A Neural Pairwise Ranking Model for Readability Assessment. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3802–3813, Dublin, Ireland. Association for Computational Linguistics.