Towards a conversational information seeking process model: Characterizing mixed‐initiative user–agent interaction

Abstract Mixed‐initiative interaction is a key feature of Conversational Information Seeking (CIS), where both users and agents actively participate in dialogue. Given the potential risks associated with agent‐initiative interactions, a CIS process model is needed to guide when and how the agent should take the initiative. Conversation analysis was employed to derive a speech act framework from the 37,268 utterances in the Wizard of Search Engine dataset. Lag sequential analysis was then used to identify adjacency pairs of speech acts with significant transition probabilities. This study identified six user acts, including querying, assessing, elaborating, reformulating, instructing, and deciding, and seven agent acts, including answering, inquiring, checking, eliciting, soliciting, informing, and offering. These acts were connected based on their adjacency, resulting in the CIS process model. This model comprises one fundamental querying‐answering sub‐process and four optional sub‐processes: need negotiation, process collaboration, result evaluation, and query elicitation. The robustness of this model was evaluated on another CIS dataset, ConvSearch, with new behavioral patterns emerging. This study identifies the multi‐stage, iterative, and dynamic nature of mixed‐initiative user–agent interaction in CIS, offers methodological insights into CIS data collection, annotation, and analysis, and provides guidance for the development and evaluation of CIS agents.