--- title: "Annotating Clinical Risk and Variation in Haitian Creole Medical" description: "Haitian Creole (HC) is badly underserved by medical machine translation. 1 reference. Among them: 0." authors: ["Ludovic Mompelat", "David Tézil", "Rose Flaure Accilien"] year: 2026 venue: "Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)" source: "https://aclanthology.org/2026.law-main.1/" doi: "10.18653/v1/2026.law-main.1" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/mompelat-2026-annotating-clinical-risk-variation-haitian/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mompelat-2026-annotating-clinical-risk-variation-haitian/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/22/) / Annotating Clinical Risk and Variation in Haitian Creole Medical Translation # Annotating Clinical Risk and Variation in Haitian Creole Medical Translation **Authors:** Ludovic Mompelat, David Tézil and Rose Flaure Accilien · **Year:** 2026 · **Venue:** [Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)](https://md.research.elamur.ai/free/en/venues/law/) ([LAW 2026](https://md.research.elamur.ai/free/en/venues/law/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.law-main.1/) · [PDF](https://aclanthology.org/2026.law-main.1.pdf) · [DOI 10.18653/v1/2026.law-main.1](https://doi.org/10.18653/v1/2026.law-main.1) · [OpenAlex](https://openalex.org/W7166837113) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary Haitian Creole (HC) is badly underserved by medical machine translation. Existing systems also ignore its internal variation, which runs from French-influenced mesolectal speech to basilectal, Creole-exclusive speech. As a result, a translation can be literally right yet confusing, stigmatizing, or clinically risky for monolingual patients, for example in Miami's diaspora. The authors cleaned and normalized the CMU Haitian Creole medical corpus and designed a compact annotation schema for small expert teams. The schema has: - binary fields for acceptability, severity of possible misunderstanding, and foreign-influence cues; - error tags that apply only when quality is flagged and are compatible with MQM; - a field for recording variety-specific variants. After three annotation-and-adjudication rounds, they released a gold set of EN→HC sentence pairs. They then tested how learnable the labels are with a simple, interpretable classifier–labeller. Acceptability and severity proved reliable for both annotators and the model. Foreign influence stayed hard to learn because it is rare in the data. The labels can already support triage of patient-facing translations, and they could later serve as reward signals for preference-based MT and LLM tuning. ## Key points - The gold dataset contains 152 adjudicated EN→HC medical sentence pairs, built from a normalized version of the CMU Haitian Creole medical corpus. Inter-annotator agreement stabilized over three rounds. - The baseline used TF–IDF and simple text-shape features with five-fold cross-validation. It outputs per-field probabilities and suggests error tags. - Severity scores worked well for ranking: checking just the items the model rated most likely to be severe caught much of the genuinely high-risk material. - Foreign influence was the weakest dimension (F1 = 0.356 ± 0.216 across folds; label prevalence 11.8%). The authors attribute this to limited data rather than a flaw in the schema. - Qualitative analysis found three recurring error types: - lexical misselection, such as stigmatizing *tafyatè*; - morphosyntactic interference from English or French; - register or variety mismatches. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [University of Alabama](https://md.research.elamur.ai/free/en/universities/university-of-alabama/) — University, Tuscaloosa, United States · 6 works · 403 citations - [University of Miami](https://md.research.elamur.ai/free/en/universities/university-of-miami/) — University, Coral Gables, United States · 2 works · 256 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [🏥 Health Professions](https://md.research.elamur.ai/free/en/shelves/health-professions/) · 75 works · [🏥 General Health Professions](https://md.research.elamur.ai/free/en/shelves/health-professions/general-health-professions/) · 14 works ## Cite Cite as: Ludovic Mompelat, David Tézil and Rose Flaure Accilien. 2026. Annotating Clinical Risk and Variation in Haitian Creole Medical Translation. Proceedings of the 20th Linguistic Annotation Workshop (LAW XX). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{mompelat2026annotating, title = {{Annotating Clinical Risk and Variation in Haitian Creole Medical Translation}}, author = {Ludovic Mompelat, David Tézil and Rose Flaure Accilien}, booktitle = {Proceedings of the 20th Linguistic Annotation Workshop (LAW XX)}, year = {2026}, doi = {10.18653/v1/2026.law-main.1}, url = {https://doi.org/10.18653/v1/2026.law-main.1} } ``` - [← previous ANNOTARES: A Dataset for Extracting Logical Structures from German Statutory Texts](https://md.research.elamur.ai/free/en/papers/schwarz-2026-annotares-dataset-extracting-logical-structures/) - [next → Annotating Indian Regional Biases using Large Language Models: Evaluation and Analysis](https://md.research.elamur.ai/free/en/papers/panda-2026-annotating-indian-regional-biases-large/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/22/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Hilal Al Shamsi, Abdullah G Almutairi, Sulaiman Al Mashrafi, and Talib Al Kalbani. 2020. Implications of language barriers for healthcare: a systematic review. Oman medical journal, 35(2):e122. [find](https://scholar.google.com/scholar?q=Hilal+Al+Shamsi%2C+Abdullah+G+Almutairi%2C+Sulaiman+Al+Mashrafi%2C+and+Talib+Al+Kalbani%2E+2020%2E+Implications+of+language+barriers+for+healthcare%3A+a+systematic+review%2E+Oman+medical+journal%2C+35%282%29%3Ae122%2E) *Updated 1 November 2026*