@inproceedings{liednikova-etal-2021-gathering,
    title = "Gathering Information and Engaging the User {C}om{B}ot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions",
    author = "Liednikova, Anna  and
      Jolivet, Philippe  and
      Durand-Salmon, Alexandre  and
      Gardent, Claire",
    editor = "Shivade, Chaitanya  and
      Gangadharaiah, Rashmi  and
      Gella, Spandana  and
      Konam, Sandeep  and
      Yuan, Shaoqing  and
      Zhang, Yi  and
      Bhatia, Parminder  and
      Wallace, Byron",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.nlpmc-1.3/",
    doi = "10.18653/v1/2021.nlpmc-1.3",
    pages = "21--29",
    abstract = "We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot."
}