A Neural Pairwise Ranking Model for Readability Assessment
This addresses readability assessment for NLP applications, offering a novel method with cross-lingual capabilities, though it is incremental in applying neural ranking to an existing task.
The paper tackles automatic readability assessment by proposing the first neural pairwise ranking model, achieving over 80% zero-shot cross-lingual accuracy for French and Spanish when trained on English data.
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.