Data for: Towards a Model for Spoken Conversational Search

Conversation is the natural mode for information exchange in daily life, a spoken conversational interaction for search input and output is a logical format for information seeking. However, the conceptualisation of user–system interactions or information exchange in spoken conversational search (SCS) has not been explored. The first step in conceptualising SCS is to understand the conversational moves used in an audio-only communication channel for search. This paper explores conversational actions for the task of search. We define a qualitative methodology for creating conversational datasets, propose analysis protocols, and develop the SCSdata. Furthermore, we use the SCSdata to create the first annotation schema for SCS: the SCoSAS, enabling us to investigate interactivity in SCS. We further establish that SCS needs to incorporate interactivity and pro-activity to overcome the complexity that the information seeking process in an audio-only channel poses. In summary, this exploratory study unpacks the breadth of SCS. Our results highlight the need for integrating discourse in future SCS models and contributes the advancement in the formalisation of SCS models and the design of SCS systems.

对话是日常生活中信息交换的自然模式，用于搜索输入与输出的口语化会话交互，是信息检索的合乎逻辑的交互形式。然而，目前尚未对口语化会话搜索（Spoken Conversational Search, SCS）中的用户-系统交互或信息交换的概念化展开研究。对口语化会话搜索进行概念化的首要步骤，是理解纯音频通信信道下用于搜索任务的会话移动行为。本研究针对搜索任务探索了会话行动范畴，定义了构建会话数据集的质性研究方法论，提出了分析规程，并开发了SCSdata数据集。进一步地，我们依托SCSdata构建了首个面向SCS的标注范式——SCoSAS，借此得以探究SCS中的交互性。此外，我们证实SCS需要融入交互性与主动性，以克服纯音频信道下信息检索流程所带来的复杂性。综上，本探索性研究厘清了口语化会话搜索的研究范畴。研究结果凸显了在未来SCS模型中整合会话话语的必要性，并推动了SCS模型的形式化进程与SCS系统的设计研发。