gokulkarthik/mucot
MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages - Accepted at ACL 2022 Workshop
This project helps improve automated question-answering systems for languages that don't have a lot of digital text data, like many in the Dravidian family. It takes existing question-and-answer pairs, translates them into other languages, and then uses a special training method to enhance the system's ability to understand and respond to questions in the original low-resource language. This is for researchers and developers building natural language processing applications for underserved languages.
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Use this if you are developing question-answering AI for languages with limited available text data and want to leverage existing models from high-resource languages.
Not ideal if you are working with languages that already have extensive datasets for question answering, or if your task is not related to question answering.
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Nov 18, 2022
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