Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach
Nadjet Bouayad-Agha, Ignasi Gomez-Sebastia, Alba Padro, Enric Pallares Roura, David Pelayo Castelló, Rocío Tovar
Abstract
Breastfeeding 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.
BibTeX
@inproceedings{bouayad-agha-etal-2024-assisting,
title = "Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an {AI}-in-the-loop Approach",
author = "Bouayad-Agha, Nadjet and
Gomez-Sebastia, Ignasi and
Padro, Alba and
Roura, Enric Pallares and
Castell{\'o}, David Pelayo and
Tovar, Roc{\'i}o",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.63/",
doi = "10.18653/v1/2024.emnlp-industry.63",
pages = "829--841"
}