2024
EMNLP
EMNLP 2024
Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach
Abstract
AbstractBreastfeeding and Maternity experts are a scarce resource and engaging in a conversation with mothers on such a sensitive topic is a time-consuming effort. We present our journey and rationale in assisting experts to answer queries about Breastfeeding and Maternity topics from users, mainly mothers. We started by developing a RAG approach to response generation where the generated response is made available to the expert who has the option to draft an answer using the generated text or to answer from scratch. This was the start of an ongoing effort to develop a pipeline of AI/NLP-based functionalities to help experts understand user queries and craft their responses.
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Topic Pioneer
— Retrieval-Augmented Generation
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— breastfeeding support
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Applications > Question Answering
Healthcare & Medicine > Clinical > Clinical NLP
Healthcare & Medicine > Clinical > Medical AI
Machine Learning > Learning Types > Retrieval-Augmented Generation
Natural Language Processing > Generation > Retrieval-Augmented Generation
Deep Learning > Learning Types > Retrieval-Augmented Generation
Healthcare & Medicine > Clinical > Medical NLP
Artificial Intelligence > Core AI > Retrieval-Augmented Generation