Facilitating Media Distribution with Monetary Incentives

Mehta, Devansh; Sharma, Alok; Mothilal, Ramaravind Kommiya; Chiraag,; Shukla, Anurag; Prasad, Vishnu; Thies, William; Udutalapally, Venkanna; Scott, Colin; Sharma, Amit

doi:10.1145/3334480.3383032

Cited by 1 publication

(1 citation statement)

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“…In order to understand these queries better, practical conversational systems for healthcare need to be developed. However, the primary obstacle in developing such technologies for low-resource languages is the lack of usable data (Mehta et al, 2020;Daniel et al, 2019;Liu, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages

Mullick¹,

Mondal²,

Ray³

et al. 2023

Preprint

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

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Section: Introductionmentioning

confidence: 99%