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.

Easy-to-Understand (E2U) language varieties have been recognized by the United Nation’s Convention on the Rights of Persons with Disabilities (2006) as a means to guarantee the fundamental right to Accessible Communication. Increased awareness has driven changes in European (European Commission, 2015, 2021; European Parliament, 2016) and International legislation (ODI, 2010), prompting public-sector and other institutions to offer domain-specific content into E2U language to prevent communicative exclusion of those facing cognitive barriers (COGA, 2017; Maaß, 2020; Perego, 2020). However, guidance on what it is that makes language actually ‘easier to understand’ is still fragmented and vague. For this reason, we carried out a systematic review of official guidelines for English Plain Language and Easy Language to identify the most effective lexical, syntactic and adaptation strategies that can reduce complexity in verbal discourse according to official bodies. This article will present the methods and preliminary results of the guidelines analysis.

In this paper, we present a semi-automated workflow for live interlingual speech-to-text communication which seeks to reduce the shortcomings of existing ASR systems: a human respeaker works with a speaker-dependent speech recognition software (e.g., Dragon Naturally Speaking) to deliver punctuated same-language output of superior quality than obtained using out-of-the-box automatic speech recognition of the original speech. This is fed into a machine translation engine (the EU’s eTranslation) to produce live-caption ready text. We benchmark the quality of the output against the output of best-in-class (human) simultaneous interpreters working with the same source speeches from plenary sessions of the European Parliament. To evaluate the accuracy and facilitate the comparison between the two types of output, we use a tailored annotation approach based on the NTR model (Romero-Fresco and Pöchhacker, 2017). We find that the semi-automated workflow combining intralingual respeaking and machine translation is capable of generating outputs that are similar in terms of accuracy and completeness to the outputs produced in the benchmarking workflow, although the small scale of our experiment requires caution in interpreting this result.