  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.
