Recommendations for Overcoming Linguistic Barriers in Healthcare: Challenges and Innovations in NLP for H aitian C reole (2025)

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.

## introduction creole languages have historically been underrepresented-and often outright ignored-in natural language processing (nlp) research @xcite . many are classified as low-resource languages due to the scarcity of annotated datasets and corpora. this is largely attributed to several factors: the limited availability of speakers for endangered creole languages, pervasive negative attitudes and stigmatization that discourage research investment, and the overall lack of theoretical and applied linguistic engagement with these languages @xcite . this neglect is particularly striking given that creole languages, as a group, are spoken by millions of people worldwide. their exclusion from nlp research increases linguistic inequalities and can limit access to crucial technologies, including healthcare-related applications. despite these challenges, there has been a growing effort to develop nlp solutions for creole languages, leading to advancements in part-ofspeech tagging, syntactic parsing, named-entity recognition, and machine translation @xcite @xcite @xcite @xcite . researchers have increasingly adopted hybrid approaches that combine traditional machine learning with more data-intensive neural and large language model (llm) techniques to address data scarcity @xcite . this hybrid approach has proven crucial for advancing nlp capabilities in lowresource contexts like creole languages. however, most existing models fail to account for linguistic variation within creoles, code-switching patterns, and domain-specific terminology-three key issues critical for real-world deployment, particularly in healthcare. due to the rapid expansion of nlp and ai research, creole researchers face a race against time and technological advances. this urgency often leads to an overemphasis on dominant varieties within specific creoles, while linguistic variation-present in all natural languages, including creoles-receives insufficient attention. variation, whether diatopic, diachronic, diastratic, or diaphasic, is frequently overlooked, resulting in general llms and machine translation systems failing to account for this diversity @xcite . this issue, described as translationese by @xcite , can negatively impact the very language communities these technologies aim to serve. a unique challenge shared by most creole languages stems from their origins and ongoing language contact situations. creole-speaking communities often exist in environments of constant interaction with another language. this contact leads to significant linguistic interference, manifesting as diglossia in some contexts or bilingualism in others. the propensity for interlingual interference results in phenomena like code-switching, borrowing, and other forms of multilingual restructuring. these dynamics highlight the critical need to incorporate linguistic variation into nlp research for creole languages, ensuring that technologies reflect their rich diversity and complex sociolinguistic realities. one domain where language access is critical is healthcare. haitian creole, the most widely spoken creole language, is vastly underrepresented in nlp, contributing to severe healthcare disparities for haitian creole-speaking communities in multilingual environments like miami. in these settings, patients frequently switch between haitian creole, french, english, and spanish, a phenomenon that existing translation systems fail to handle effectively. additionally, formal medical discourse differs significantly from everyday conversational haitian creole, further complicating automatic translation efforts. the lack of medical translation tools tailored to haitian creole leads to miscommunication between healthcare providers and patients, which has been linked to misdiagnoses, non-compliance with treatment plans, and preventable health complications. addressing this issue requires nlp models that accurately capture creole linguistic variation, handle multilingual and code-switched text, and integrate standardized medical terminology-none of which are adequately covered by current haitian creole language models. this work proposes a structured methodology for developing an ai-assisted translation and interpretation tool specifically designed for healthcare communication. our approach prioritizes linguistic variation, code-switching, and domain-specific adaptation to create a culturally and contextsensitive nlp system. to achieve this, we: by addressing these linguistic and computational challenges, this project contributes to both nlp research and healthcare equity. it also provides a scalable framework for other low-resource languages facing similar issues in medical translation, multilingual communication, and linguistic variation. ## background and related work haitian creole exhibits significant linguistic variation, including basilectal and mesolectal varieties influenced by french and other languages. the basilect-mesolect-acrolect continuum in creolespeaking territories describes the range of language varieties, from the most creole-like variety (the basilect) to the variety most closely resembling the european lexifier language (in this case, french), referred to as the acrolect. the mesolect, or mesolectal zone, serves as an intermediary area encompassing a blend of phonological, lexical, morphosyntactic, and semantic features from both the basilect and acrolect @xcite . in this context, linguists have characterized the mesolect as containing a creole-based variety influenced by the acrolect, sometimes described as a "frenchified creole." in haiti, this variety is known as kréyòl swa @xcite . conversely, the continuum also includes a local french variety influenced by the basilect, often termed "creolized french." of particular interest to this work is the relationship between the basilectal variety, known as krèyòl rèk, and kréyòl swa within the linguistic continuum. krèyòl rèk is predominantly spoken by monolingual haitian creole speakers in haiti and possesses distinctive features that set it apart from kréyòl swa, which is primarily used by bilingual haitian creole-french speakers in haiti and its diaspora. due to their numerous structural differences, these two varieties need to be treated as two distinct linguistic units. krèyòl rèk is often associated with lower prestige and is viewed as the most authentic representation of the basilectal variety, while kréyòl swa carries higher prestige due to its proximity to french. these dynamics reflect deeper sociolinguistic patterns tied to language, identity, and power in haitian society @xcite . among nlp initiatives and llm developments for haitian creole and other creole languages, several notable contributions stand out. @xcite introduced creoleval, a multilingual benchmark for creole languages and lent et al. (2022) proposed guidelines for developing nlp technologies for creole languages. older but equally important initiatives include the haitian creole language data by carnegie mellon 1 , which contains medical domain phrases and sentences; the universal dependencies (ud) haitian creole autogramm treebank (jagodzińska et al.) 2 , with sentences sourced from the bible, novels, and newspapers; and the leipzig corpora collection 3 1 http://www.speech.cs.cmu.edu/haitian/ 2 https://github.com/ universaldependencies/ud_haitian_ creole-autogramm -accessed:2024-12-07 3 https://corpora.uni-leipzig.de? @xmath0:2024-a scraped haitian creole corpus primarily composed of wikipedia articles. additionally, mainstream multilingual llms, such as mbert @xcite , xlm-r @xcite , mt5 @xcite , and m2m-100 @xcite , include haitian creole as part of their training data. despite these contributions, no existing model sufficiently addresses the specific challenges of our task. variation is a critical component of nlp tasks for haitian creole due to the complexity of its linguistic landscape, which spans multiple varieties and contact languages. current models often fail to encompass the linguistic diversity of haitian creole, whether within haiti or the broader diaspora. while linguistic variation can theoretically be "learned" by systems through extensive data, the scarcity of annotated resources for underrepresented languages like haitian creole renders this approach ineffective. another significant limitation of existing models is their lack of robust accuracy in handling code-switching effectively for language pairs containing low-resource languages in particular @xcite @xcite . this issue is also particularly pronounced in multilingual environments like miami, where haitian creole speakers frequently switch between haitian creole, french, english, and spanish. finally, there is a lack of resources and models specifically designed for haitian creole in domain-specific contexts, such as healthcare. this gap is compounded by broader challenges in extending creole languages beyond their traditionally established functions, particularly in scientific and technical domains. a notable effort in this regard is the mit-ayiti lab's initiative to create new vocabulary for stem materials 4 . however, this project faced criticism from linguists for its reliance on the lexifier language (french) to generate new haitian creole terms, which sparked debates about linguistic authenticity and community acceptance 5 . this project therefore requires a series of targeted steps to address the multifaceted challenges of developing an accurate and culturally sensitive machine translation (mt) model for haitian creole speakers in miami's healthcare context. by leveraging existing research and creating new task-specific nlp resources, we aim to tackle the following critical issues: ## modeling approach the modeling approach for this project builds upon recommendations from @xcite , combining traditional machine learning methods with modern transformer-based techniques. they point out that traditional classifiers, such as support vector machines (svms), have proven effective in distinguishing closely related languages but that advancements in contextual embedding models, particularly bert, have outperformed traditional methods in tasks requiring nuanced language understanding. for haitian creole, multilingual transformerbased models (e.g., mbert, xlm-r) offer significant potential to handle linguistic complexity and code-switching. this section outlines strategies to adapt and fine-tune these models to the unique challenges of haitian creole in healthcare contexts. ## evaluation and mitigation of bias in domain-specific tasks ensuring fairness and accuracy in nlp models tailored for haitian creole, particularly in domainspecific tasks like healthcare communication, requires a comprehensive evaluation framework. this framework must address linguistic variation, code-switching, and the unique demands of domain-specific applications. by carefully designing evaluation criteria and incorporating iterative improvements, this section outlines a strategy to assess model performance while identifying and mitigating biases that may affect the utility and inclusivity of the system. ## scalability and generalization to nlp field to ensure the scalability and adaptability of the methodologies developed, the model must be tested with haitian creole-speaking populations in various contexts, including haiti, the wider caribbean, and diaspora communities across north america and beyond. testing across these diverse linguistic and cultural environments will help validate the tool's flexibility in capturing regional and sociolinguistic nuances. the outcomes of this testing will also provide valuable insights into the scalability of the framework to other under-resourced languages. many such languages share challenges similar to those faced by haitian creole, such as limited availability of annotated datasets, significant regional variation, and a lack of domainspecific corpora.