%0 Conference Proceedings %T Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language %A Miliani, Martina %A Auriemma, Serena %A Alva-Manchego, Fernando %A Lenci, Alessandro %Y He, Yulan %Y Ji, Heng %Y Li, Sujian %Y Liu, Yang %Y Chang, Chua-Hui %S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) %D 2022 %8 November %I Association for Computational Linguistics %C Online only %F miliani-etal-2022-neural %X Automatic Readability Assessment aims at assigning a complexity level to a given text, which could help improve the accessibility to information in specific domains, such as the administrative one. In this paper, we investigate the behavior of a Neural Pairwise Ranking Model (NPRM) for sentence-level readability assessment of Italian administrative texts. To deal with data scarcity, we experiment with cross-lingual, cross- and in-domain approaches, and test our models on Admin-It, a new parallel corpus in the Italian administrative language, containing sentences simplified using three different rewriting strategies. We show that NPRMs are effective in zero-shot scenarios (~0.78 ranking accuracy), especially with ranking pairs containing simplifications produced by overall rewriting at the sentence-level, and that the best results are obtained by adding in-domain data (achieving perfect performance for such sentence pairs). Finally, we investigate where NPRMs failed, showing that the characteristics of the training data, rather than its size, have a bigger effect on a model’s performance. %U https://aclanthology.org/2022.aacl-main.63 %P 849-866