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Scarcity of data and technological limitations for resource-poor languages indeveloping countries like India poses a threat to the development ofsophisticated NLU systems for healthcare. To assess the current status ofvarious state-of-the-art language models in healthcare, this paper studies theproblem by initially proposing two different Healthcare datasets, IndianHealthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one realworld Indian hospital query data in English and multiple Indic languages(Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated withthe query intents as well as entities. Our aim is to detect query intents andextract corresponding entities. We perform extensive experiments on a set ofmodels in various realistic settings and explore two scenarios based on theaccess to English data only (less costly) and access to target language data(more expensive). We analyze context specific practical relevancy throughempirical analysis. The results, expressed in terms of overall F1 score showthat our approach is practically useful to identify intents and entities.

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages