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Accessible Communication: a systematic review and comparative analysis of official English Easy-to-Understand (E2U) language guidelines

Paper · 2024 · Andreea Maria Deleanu, Constantin Orasan and Sabine Braun

Summary

Easy-to-Understand (E2U) language is supported by the UNCRPD and by European law, but practical advice on what makes a text easier to understand is scattered, vague and often contradictory. The authors systematically reviewed 10 official English guidelines for Plain Language and Easy Language. They sorted each guideline's advice into lexical categories (generic words, nouns, referents, adverbs and adjectives, verbs) and syntactic categories (sentence length, word order, ideas per sentence, punctuation). For every category they marked whether a guideline recommends a feature, discourages it, or does not mention it. A qualitative comparison then identified points of agreement, conflicts and "grey areas" that no guideline covers clearly. The main finding is that the guidelines share some core advice but differ widely in coverage and precision. Some even break their own rules. The work lays the foundation for an audiovisual accessibility mode called "Accessible Cues", which uses on-screen text and an extra narrator to explain films to cognitively diverse audiences. It also informs a planned corpus study of how professionals actually adapt texts.

Key points

  • The 10 guidelines are Mencap (2000), McGee (2010), PLAIN (2011a), GDS (2022), ILSMH (1998), Inclusion Europe (2010), IFLA (2010), ODI (2010), Scope (2015) and Change (2016).
  • Recommendations are compared in a feature-by-guideline grid. Lexical features include jargon, abbreviations, acronyms, figures of speech, passive voice, negation, modal verbs and tenses; syntactic features include sentence length, SVO order and punctuation.
  • Advice on sentence length varies widely. Suggestions range from 5–8 words for readers with moderate learning disabilities, through 8–15 or 10–15 words, up to a maximum of 25. One guideline gives line-length limits instead: 25–30 characters per line next to images, otherwise 50–60.
  • Inclusion Europe (2010) is shown to break its own advice on negations, contractions, past tense and passives within its own text.
  • The findings feed a later analysis of two parallel standard-vs-adapted corpora: Guardian Weekly / Onestopenglish news and the FIRST project corpus.

Interesting ideas

  • I1. Turning prose guidelines into a matrix that marks each feature as recommended, discouraged or silent makes agreement, conflict and gaps visible at once.
  • I2. Checking whether a guideline's own text obeys its rules is a cheap way to measure how consistent and realistic the rules are.
  • I3. Comparing prescriptive advice with parallel standard-vs-adapted corpora separates what experts say from what adaptors actually do.
  • I4. The advice to replace an unclear pronoun with the proper name links readability directly to reference resolution.
  • I5. The split between intrinsic complexity (how a text is built) and extrinsic complexity (the reader's cognitive resources) separates text properties from reader properties.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (back): Express our text-simplification and generation constraints as such a feature matrix, so every rewrite rule can be traced to a stated source and gaps are easy to spot.
  • I2 (learn): Use this as a negative control by running the readability or repair rules on texts written by the people who wrote the rules, and flag rules that even those texts violate.
  • I3 (learn): Align Onestopenglish or FIRST sentence pairs through their UD trees to find the structural edits that adaptors really make, such as splitting clauses or turning passives into actives.
  • I4 (frames): When coreference confidence for a pronoun is low, mark it so the generator repeats the entity's name instead of the pronoun.
  • I5 (answer): Model reader profiles separately from text difficulty scores, so explanations given to students can be adjusted without changing the underlying meaning graph.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksHealth Professions · 69 worksGeneral Health Professions · 14 worksHuman Factors and Ergonomics · 4 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,263 works

Cite

Cite as Andreea Maria Deleanu, Constantin Orasan and Sabine Braun. 2024. Accessible Communication: a systematic review and comparative analysis of official English Easy-to-Understand (E2U) language guidelines. Proceedings of the 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) @ LREC-COLING 2024.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{deleanu2024accessible,
  title = {{Accessible Communication: a systematic review and comparative analysis of official English Easy-to-Understand (E2U) language guidelines}},
  author = {Andreea Maria Deleanu, Constantin Orasan and Sabine Braun},
  booktitle = {Proceedings of the 3rd Workshop on Tools and Resources for People with REAding DIfficulties (READI) @ LREC-COLING 2024},
  year = {2024},
  doi = {10.63317/5cmzpkerhq6t},
  url = {https://doi.org/10.63317/5cmzpkerhq6t}
}

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  54. Figure C: Example of how inferences should be spelled out in healthcare materials (McGee, 2010) Domain & txt type End-users find

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