In conversational settings, individuals exhibit unique behaviors, rendering a one-size-fits-all approach insufficient for generating responses by dialogue agents. Although past studies have aimed to create personalized dialogue agents using speaker persona information, they have relied on the assumption that the speaker's persona is already provided. However, this assumption is not always valid, especially when it comes to chatbots utilized in industries like banking, hotel reservations, and airline bookings. This research paper aims to fill this gap by exploring the task of Speaker Profiling in Conversations (SPC). The primary objective of SPC is to produce a summary of persona characteristics for each individual speaker present in a dialogue. To accomplish this, we have divided the task into three subtasks: persona discovery, persona-type identification, and persona-value extraction. Given a dialogue, the first subtask aims to identify all utterances that contain persona information. Subsequently, the second task evaluates these utterances to identify the type of persona information they contain, while the third subtask identifies the specific persona values for each identified type. To address the task of SPC, we have curated a new dataset named SPICE, which comes with specific labels. We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper. Furthermore, we present a comprehensive analysis of SPOT, examining the limitations of individual modules both quantitatively and qualitatively.

翻译：在对话场景中，个体展现出独特的行为特征，这使得通用型方法难以满足对话代理生成个性化回复的需求。尽管已有研究尝试利用说话人身份信息构建个性化对话代理，但这些工作通常假设说话人的身份特征已预先提供。然而这一假设在现实场景中常不成立，尤其是在银行客服、酒店预订及机票订购等行业的聊天机器人应用场景中。本研究旨在填补这一空白，探索对话中的说话人画像分析（SPC）任务。SPC的核心目标是为对话中每位说话人生成其身份特征摘要。为实现该目标，我们将任务分解为三个子任务：身份发现、身份类型识别与身份值抽取。首先，第一个子任务从对话中识别出所有包含身份信息的语句；其次，第二个子任务评估这些语句以判定其包含的身份信息类型；最后，第三个子任务针对每个已识别类型抽取具体的身份值。为支持SPC任务研究，我们构建了包含特定标注的新数据集SPICE，并在该数据集上评估了多种基线方法，同时引入本文提出的新型神经模型SPOT作为基准。此外，我们还从定量和定性两个维度对SPOT进行了全面分析，深入剖析各模块的局限性。