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Comprehensibility and Automation: Plain Language in the Era of Digitalization

Comprehensibility and Automation: Plain Language in the Era of Digitalization AbstractThe current article briefly presents a pilot machine-learning experiment on the classification of official texts addressed to lay readers with the use of support vector machine as a baseline and fastText models.For this purpose, a hand-crafted corpus was used, created by the experts of the National Tax and Customs Administration of Hungary under the office’s Public Accessibility Programme. The corpus contained sentences that were paraphrased or completely rewritten by the experts to make them more readable for lay people, as well their original counter pairs. The aim was to automatically distinguish between these two classes by using supervised machine-learning algorithms.If successful, such a machine-learning-based model could be used to draw the attention of experts involved in making the texts of official bodies more comprehensible to the average reader to the potentially problematic points of a text. Therefore, the process of rephrasing such texts could be sped up drastically.Such a rephrasing (considering, above all, the needs of the average reader) can improve the overall comprehensibility of official (mostly legal) texts, and therefore supports access to justice, the transparency of governmental organizations and, most importantly, improves the rule of law in a given country. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Baltic Journal of European Studies de Gruyter

Comprehensibility and Automation: Plain Language in the Era of Digitalization

Üveges, István
Baltic Journal of European Studies , Volume 12 (2): 23 – Dec 1, 2022
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Publisher
de Gruyter
Copyright
© 2022 István Üveges, published by Sciendo
ISSN
2228-0596
eISSN
2674-4619
DOI
10.2478/bjes-2022-0012
Publisher site
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Abstract

AbstractThe current article briefly presents a pilot machine-learning experiment on the classification of official texts addressed to lay readers with the use of support vector machine as a baseline and fastText models.For this purpose, a hand-crafted corpus was used, created by the experts of the National Tax and Customs Administration of Hungary under the office’s Public Accessibility Programme. The corpus contained sentences that were paraphrased or completely rewritten by the experts to make them more readable for lay people, as well their original counter pairs. The aim was to automatically distinguish between these two classes by using supervised machine-learning algorithms.If successful, such a machine-learning-based model could be used to draw the attention of experts involved in making the texts of official bodies more comprehensible to the average reader to the potentially problematic points of a text. Therefore, the process of rephrasing such texts could be sped up drastically.Such a rephrasing (considering, above all, the needs of the average reader) can improve the overall comprehensibility of official (mostly legal) texts, and therefore supports access to justice, the transparency of governmental organizations and, most importantly, improves the rule of law in a given country.

Journal

Baltic Journal of European Studies – de Gruyter

Published: Dec 1, 2022

Keywords: access to justice; applied linguistics; comprehensibility; machine learning; plain language

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