@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://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.63/",
    doi = "10.18653/v1/2024.emnlp-industry.63",
    pages = "829--841",
    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."
}