Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic?

Proceedings of the 29th International Conference on Computational Linguistics, pages 4320–4335 October 12–17, 2022. 4320 Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic? ♡Ishani Mondal∗, ♡Kabir Ahuja∗, ♡Mohit Jain, ♢Jacki O’Neil, ♡Kalika Bali, ♡Monojit Choudhury ♡Microsoft Research Labs, Bangalore, India ♢Microsoft Africa Research Institute, Nairobi, Kenya {t-imonda, t-kabirahuja, kalikab, monojitc}@microsoft.com Abstract The COVID-19 pandemic has brought out both the best and worst of language technology (LT). On one hand, conversational agents for infor- mation dissemination and basic diagnosis have seen widespread use, and arguably, had an im- portant role in fighting against the pandemic. On the other hand, it has also become clear that such technologies are readily available for a handful of languages, and the vast majority of the global south is completely bereft of these benefits. What is the state of LT, especially conversational agents, for healthcare across the world’s languages? And, what would it take to ensure global readiness of LT before the next pandemic? In this paper, we try to answer these questions through survey of existing literature and resources, as well as through a rapid chat- bot building exercise for 15 Asian and African languages with varying amount of resource- availability. The study confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help us prioritize research and investment strategies in LT for healthcare. 1 Introduction The world witnessed one of the worst pandemics in early 2020, COVID-19, infecting over 250 million people globally. Scientists and technologists from various fields joined hands, lending support to deal with this global crisis. Language Technology (LT), particularly the conversational agents (aka chat- bots), played a crucial role during the pandemic by facilitating correct information dissemination (Li et al., 2020; Maniou and Veglis, 2020) and early disease screening (Judson et al., 2020a; Martin et al., 2020b). Nevertheless, today practically use- ful chatbots and other benefits of LT are available only in a handful of languages (Joshi et al., 2020). Despite impressive gains made by the Massively Multilingual Transformer based Language Models ∗Equal contribution (MMLM) (Devlin et al., 2019; Lample and Con- neau, 2019; Aharoni et al., 2019; Conneau et al., 2020; Xue et al., 2021) on standard NLP bench- mark tasks (Pan et al., 2017; Conneau et al., 2018; Yang et al., 2019; Ruder et al., 2021), the real- world implications of such advancements remain largely unexplored. Joshi et al. (2020) has high- lighted such a disparity and proposed a language hierarchy that comprises of the languages of world classified into six classes based on their resource- availability. In this hierarchy, Class 5 represents the most resource-rich languages for whom benefits of LT are readily available; and class 0 denotes the most under-resourced languages. In this paper, we ask the following two questions: (1) Today, in which languages can we build practi- cally useful LT systems, especially chatbots, that could serve as beneficial assistants during the pan- …

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