<abstract xmlns="http://www.w3.org/1999/xhtml">

The 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.
</abstract>

The 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.

Comprehensibility and Automation: Plain Language in the Era of Digitalization

TalTech Journal of European Studies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Comprehensibility and Automation: Plain Language in the Era of Digitalization"}

The current article briefly presents a pilot machine-learning experiment on the classification of official texts addressed to lay readers with the use of...

Comprehensibility and Automation: Plain Language in the Era of Digitalization

Online veröffentlicht: 29. Dez. 2022

Seitenbereich: 64 - 86

DOI: https://doi.org/10.2478/bjes-2022-0012

Schlüsselwörter
access to justice, applied linguistics, comprehensibility, machine learning, plain language

© 2022 István Üveges, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Comprehensibility and Automation: Plain Language in the Era of Digitalization

Online veröffentlicht: 29. Dez. 2022

Seitenbereich: 64 - 86

DOI: https://doi.org/10.2478/bjes-2022-0012

Schlüsselwörteraccess to justice, applied linguistics, comprehensibility, machine learning, plain language

© 2022 István Üveges, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
access to justice, applied linguistics, comprehensibility, machine learning, plain language