@inproceedings{deleanu-etal-2025-professionally,
title = "Do professionally adapted texts follow existing Easy-to-Understand ({E}2{U}) language guidelines? A quantitative analysis of two professionally adapted corpora",
author = "Deleanu, Andreea and
Or{\u{a}}san, Constantin and
Qian, Shenbin and
Bezobrazova, Anastasiia and
Braun, Sabine",
editor = "Ginel, Mar{\'i}a Isabel Rivas and
Cadwell, Patrick and
Canavese, Paolo and
Hansen-Schirra, Silvia and
Kappus, Martin and
Matamala, Anna and
Noonan, Will",
booktitle = "Proceedings of the 1st Workshop on Artificial Intelligence and Easy and Plain Language in Institutional Contexts (AI {\&} EL/PL)",
month = jun,
year = "2025",
address = "Geneva, Switzerland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2025.aielpl-1.8/",
pages = "73--93",
ISBN = "978-2-9701897-5-6",
abstract = "Easy-to-Understand (E2U) language varieties have been recognized by the UN Convention on the Rights of Persons with Disabilities as a means to prevent communicative exclusion of those facing cognitive barriers and guarantee the fundamental right to Accessible Communication. However, guidance on what it is that makes language `easier to understand' is still fragmented and vague, leading practitioners to rely on their individual expertise. For this reason, this article presents a quantitative corpus analysis to further understand which features of E2U language can more effectively improve verbal comprehension according to professional practice. This is achieved by analysing two parallel corpora of standard and professionally adapted E2U articles to identify adaptation practices implemented according to, in spite of or in addition to official E2U guidelines (Deleanu et al., 2024). The results stemming from the corpus analysis, provide insight into the most effective adaptation strategies that can reduce complexity in verbal discourse. This article will present the methods and results of the corpus analysis."
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%0 Conference Proceedings
%T Do professionally adapted texts follow existing Easy-to-Understand (E2U) language guidelines? A quantitative analysis of two professionally adapted corpora
%A Deleanu, Andreea
%A Orăsan, Constantin
%A Qian, Shenbin
%A Bezobrazova, Anastasiia
%A Braun, Sabine
%Y Ginel, María Isabel Rivas
%Y Cadwell, Patrick
%Y Canavese, Paolo
%Y Hansen-Schirra, Silvia
%Y Kappus, Martin
%Y Matamala, Anna
%Y Noonan, Will
%S Proceedings of the 1st Workshop on Artificial Intelligence and Easy and Plain Language in Institutional Contexts (AI & EL/PL)
%D 2025
%8 June
%I European Association for Machine Translation
%C Geneva, Switzerland
%@ 978-2-9701897-5-6
%F deleanu-etal-2025-professionally
%X Easy-to-Understand (E2U) language varieties have been recognized by the UN Convention on the Rights of Persons with Disabilities as a means to prevent communicative exclusion of those facing cognitive barriers and guarantee the fundamental right to Accessible Communication. However, guidance on what it is that makes language ‘easier to understand’ is still fragmented and vague, leading practitioners to rely on their individual expertise. For this reason, this article presents a quantitative corpus analysis to further understand which features of E2U language can more effectively improve verbal comprehension according to professional practice. This is achieved by analysing two parallel corpora of standard and professionally adapted E2U articles to identify adaptation practices implemented according to, in spite of or in addition to official E2U guidelines (Deleanu et al., 2024). The results stemming from the corpus analysis, provide insight into the most effective adaptation strategies that can reduce complexity in verbal discourse. This article will present the methods and results of the corpus analysis.
%U https://aclanthology.org/2025.aielpl-1.8/
%P 73-93
Markdown (Informal)
[Do professionally adapted texts follow existing Easy-to-Understand (E2U) language guidelines? A quantitative analysis of two professionally adapted corpora](https://aclanthology.org/2025.aielpl-1.8/) (Deleanu et al., AIELPL 2025)
ACL