@inproceedings{mompelat-2025-recommendations,
title = "Recommendations for Overcoming Linguistic Barriers in Healthcare: Challenges and Innovations in {NLP} for {H}aitian {C}reole",
author = "Mompelat, Ludovic",
editor = "Holdt, {\v{S}}pela Arhar and
Ilinykh, Nikolai and
Scalvini, Barbara and
Bruton, Micaella and
Debess, Iben Nyholm and
Tudor, Crina Madalina",
booktitle = "Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)",
month = mar,
year = "2025",
address = "Tallinn, Estonia",
publisher = "University of Tartu Library, Estonia",
url = "https://aclanthology.org/2025.resourceful-1.6/",
pages = "20--31",
ISBN = "978-9908-53-121-2",
abstract = "Haitian Creole, spoken by millions in Haiti and its diaspora, remains underrepresented in Natural Language Processing (NLP) research, limiting the availability of effective translation tools. In Miami, a significant Haitian Creole-speaking population faces healthcare disparities exacerbated by language barriers. Existing translation systems fail to address key challenges such as linguistic variation within the Creole language, frequent code-switching, and the lack of standardized medical terminology. This work proposes a structured methodology for the development of an AI-assisted translation and interpretation tool tailored for patient-provider communication in a medical setting. To achieve this, we propose a hybrid NLP approach that integrates fine-tuned Large Language Models (LLMs) with traditional machine translation methods. This combination ensures accurate, context-sensitive translation that adapts to both formal medical discourse and conversational registers while maintaining linguistic consistency. Additionally, we discuss data collection strategies, annotation challenges, and evaluation metrics necessary for building an ethically designed, scalable NLP system. By addressing these issues, this research provides a foundation for improving healthcare accessibility and linguistic equity for Haitian Creole speakers."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mompelat-2025-recommendations">
<titleInfo>
<title>Recommendations for Overcoming Linguistic Barriers in Healthcare: Challenges and Innovations in NLP for Haitian Creole</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ludovic</namePart>
<namePart type="family">Mompelat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Špela</namePart>
<namePart type="given">Arhar</namePart>
<namePart type="family">Holdt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nikolai</namePart>
<namePart type="family">Ilinykh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Barbara</namePart>
<namePart type="family">Scalvini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Micaella</namePart>
<namePart type="family">Bruton</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iben</namePart>
<namePart type="given">Nyholm</namePart>
<namePart type="family">Debess</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Crina</namePart>
<namePart type="given">Madalina</namePart>
<namePart type="family">Tudor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>University of Tartu Library, Estonia</publisher>
<place>
<placeTerm type="text">Tallinn, Estonia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">978-9908-53-121-2</identifier>
</relatedItem>
<abstract>Haitian Creole, spoken by millions in Haiti and its diaspora, remains underrepresented in Natural Language Processing (NLP) research, limiting the availability of effective translation tools. In Miami, a significant Haitian Creole-speaking population faces healthcare disparities exacerbated by language barriers. Existing translation systems fail to address key challenges such as linguistic variation within the Creole language, frequent code-switching, and the lack of standardized medical terminology. This work proposes a structured methodology for the development of an AI-assisted translation and interpretation tool tailored for patient-provider communication in a medical setting. To achieve this, we propose a hybrid NLP approach that integrates fine-tuned Large Language Models (LLMs) with traditional machine translation methods. This combination ensures accurate, context-sensitive translation that adapts to both formal medical discourse and conversational registers while maintaining linguistic consistency. Additionally, we discuss data collection strategies, annotation challenges, and evaluation metrics necessary for building an ethically designed, scalable NLP system. By addressing these issues, this research provides a foundation for improving healthcare accessibility and linguistic equity for Haitian Creole speakers.</abstract>
<identifier type="citekey">mompelat-2025-recommendations</identifier>
<location>
<url>https://aclanthology.org/2025.resourceful-1.6/</url>
</location>
<part>
<date>2025-03</date>
<extent unit="page">
<start>20</start>
<end>31</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Recommendations for Overcoming Linguistic Barriers in Healthcare: Challenges and Innovations in NLP for Haitian Creole
%A Mompelat, Ludovic
%Y Holdt, Špela Arhar
%Y Ilinykh, Nikolai
%Y Scalvini, Barbara
%Y Bruton, Micaella
%Y Debess, Iben Nyholm
%Y Tudor, Crina Madalina
%S Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)
%D 2025
%8 March
%I University of Tartu Library, Estonia
%C Tallinn, Estonia
%@ 978-9908-53-121-2
%F mompelat-2025-recommendations
%X Haitian Creole, spoken by millions in Haiti and its diaspora, remains underrepresented in Natural Language Processing (NLP) research, limiting the availability of effective translation tools. In Miami, a significant Haitian Creole-speaking population faces healthcare disparities exacerbated by language barriers. Existing translation systems fail to address key challenges such as linguistic variation within the Creole language, frequent code-switching, and the lack of standardized medical terminology. This work proposes a structured methodology for the development of an AI-assisted translation and interpretation tool tailored for patient-provider communication in a medical setting. To achieve this, we propose a hybrid NLP approach that integrates fine-tuned Large Language Models (LLMs) with traditional machine translation methods. This combination ensures accurate, context-sensitive translation that adapts to both formal medical discourse and conversational registers while maintaining linguistic consistency. Additionally, we discuss data collection strategies, annotation challenges, and evaluation metrics necessary for building an ethically designed, scalable NLP system. By addressing these issues, this research provides a foundation for improving healthcare accessibility and linguistic equity for Haitian Creole speakers.
%U https://aclanthology.org/2025.resourceful-1.6/
%P 20-31
Markdown (Informal)
[Recommendations for Overcoming Linguistic Barriers in Healthcare: Challenges and Innovations in NLP for Haitian Creole](https://aclanthology.org/2025.resourceful-1.6/) (Mompelat, RESOURCEFUL 2025)
ACL