Gathering Information and Engaging the User ComBot : A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions

Anna Liednikova (1) , Philippe Jolivet (2) , Alexandre Durand-Salmon (2) , Claire Gardent (1)

1 SYNALP - Natural Language Processing : representations, inference and semantics
2 ALIAE

We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the closed set of 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 efficient manner that an ensemble model combining the information seeking and the social bot.

Domaines: Intelligence artificielle [cs.AI]

Anna Liednikova, Philippe Jolivet, Alexandre Durand-Salmon, Claire Gardent. Gathering Information and Engaging the User ComBot : A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions. NLPMC 2021 - Second Workshop on Natural Language Processing for Medical Conversations, Jun 2021, Mexico, Mexico. pp.21-29. ⟨hal-03461330⟩