Take Shelter, Zanmi: Digitally Alerting Cyclone Victims in Their Languages (2025)

Abstract: Natural disasters such as tropical cyclones cause annual devastation and take a heavy so- cial cost, as disadvantaged communities are typ- ically hit hardest. Among these communities are the speakers of minority and low-resource languages, who may not be sufficiently in- formed about incoming weather events to pre- pare. This work presents an analysis of the current state of machine translation for natural disasters in the languages of communities that are threatened by them. Results suggest that commercial systems are promising, and that in-genre fine-tuning data are beneficial.

## introduction and related work natural disasters are among the most outstanding humanitarian crises in the 21st century @xcite . the propensity of atlantic hurricanes has been increasing in recent years and is likely to continue, due to effects of climate change @xcite . hurricanes and cyclones can be particularly destructive. according to the us office for coastal management, foot_0 tropical cyclones in the usa have cost over @xmath01 billion, or roughly 15% of its gdp at the time @xcite . cyclones also cause tragic loss of life. 2017's hurricane maria caused ∼3,000 deaths in puerto rico and the lesser antilles @xcite . as a preventative measure for these types of tragedies, political leaders often issue evacuation image source: the washington post @xcite . evacuation areas in lee and collier counties were later expanded @xcite . the effects of this phenomenon can vary by tropical storm. for instance, when hurricane irma struck florida on 10 september 2017, the state's then-governor, rick scott, had already declared a state of emergency six days prior @xcite . the six days' notice provided many civilians ample time to prepare for the storm. however, this also meant that those who prepared first claimed a majority of supplies early on. in the final days leading up to the storm, many grocery store shelves and gas station tanks were empty; leaving late preparers with few options @xcite . florida's areas of mandatory evacuation due to irma are illustrated in figure 1 . during the crisis of hurricane matthew, which struck florida a year earlier, in 2016, then-us president barack obama had declared a state of emergency in the state only three days before the storm struck @xcite . this gave civilians less time to prepare, but also resulted in a shorter period of resource scarceness. these decisions depend on numerous factors, including storm trajectory and speed (regnier, 2020). since the majority of floridan news reports, alerts, and social media posts are in english, english speakers were often the first to be informed of these crises; offering them a clear advantage over their non-english-speaking (predominantly hispanic and haitian) neighbors in claiming emergency preparedness resources @xcite . phenomena like these are part of why non-english-speaking communities in the usa are often the most harmed by natural disasters @xcite . translation is needed to mitigate this, which can be expedited computationally when human translation resources are scarce or slow, via machine translation (mt). in this work, we explore two principal questions: (1) what mt systems are best applied in disaster scenarios, and under what circumstances? and (2) what mt model training practices contribute to success in this domain? other researchers have explored similar topics. in the wake of haiti's devastating 2010 earthquake, there arose a renewed interest in haitian mt for natural disaster relief and humanitarian aid @xcite . this interest inspired a task at the workshop on statistical machine translation (wmt) the following year: mt for the haitian language @xcite . this task and the data set released with it led to subsequent works in haitian mt @xcite @xcite . additional research has focused on mt for natural disaster communication @xcite , including multilingual systems that extend processing beyond translation @xcite . we add to these previous works with a more current study focusing on mt into and out of english for four low-resource languages of cyclone-affected communities (haitian, jamaican patois, antillean creole, and mauritian creole). we contribute: ## methodology to analyze the state of mt of natural disaster alerts into the low-resource languages of affected areas, we evaluate on three test suites: (1) a set of haitian sms text messages sent during haiti's 2010 earthquake with paired english translations; ## experimental results and conclusion in this evaluation, the closed-source commercial model performs best, followed by the finetuned kreyòl-mt model. this highlights commercial models as strong choices for deployment of haitian disaster mt, and suggests that the creolelanguage-specific kreyòl-mt model can surpass nllb, when both are fine-tuned on in-genre data. a brief human evaluation for haitian confirms this trend. we had a proficient haitian speaker foot_6 blindly rate translations' accuracy and fluency for 100 utterances. from this evaluation, google's average score was 4.55, where 5 signifies "no semantic errors, or like native"; and 4 signifies "semantic errors require minor fixes, or understandable @xcite , while google's model may have been exposed to a large amount. in conclusion, humanitarian good can be accomplished by turning digital applications to help language communities in crisis. in our evaluation of mt in natural disasters for language communities that often fall prey to cyclones, we have found that commercial systems show promise for this application, that fine-tuning open source models on in-domain data improves results, and that mixed or discourse data is more beneficial for fine-tuning towards this task than bible data.