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        <title>Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language</title>
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    <abstract>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.</abstract>
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%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
%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

Markdown (Informal)

[Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language](https://aclanthology.org/2022.aacl-main.63) (Miliani et al., AACL-IJCNLP 2022)

• Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language (Miliani et al., AACL-IJCNLP 2022)

ACL

• Martina Miliani, Serena Auriemma, Fernando Alva-Manchego, and Alessandro Lenci. 2022. Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language. In 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), pages 849–866, Online only. Association for Computational Linguistics.