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    <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.</abstract>
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%0 Conference Proceedings
%T Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach
%A Bouayad-Agha, Nadjet
%A Gomez-Sebastia, Ignasi
%A Padro, Alba
%A Roura, Enric Pallares
%A Castelló, David Pelayo
%A Tovar, Rocío
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F bouayad-agha-etal-2024-assisting
%X 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.
%U https://aclanthology.org/2024.emnlp-industry.63
%P 829-841

Markdown (Informal)

[Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach](https://aclanthology.org/2024.emnlp-industry.63) (Bouayad-Agha et al., EMNLP 2024)

• Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach (Bouayad-Agha et al., EMNLP 2024)

ACL

• Nadjet Bouayad-Agha, Ignasi Gomez-Sebastia, Alba Padro, Enric Pallares Roura, David Pelayo Castelló, and Rocío Tovar. 2024. Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 829–841, Miami, Florida, US. Association for Computational Linguistics.