TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview
CoRR(2024)
摘要
Conversational Information Seeking stands as a pivotal research area with
significant contributions from previous works. The TREC Interactive Knowledge
Assistance Track (iKAT) builds on the foundational work of the TREC
Conversational Assistance Track (CAsT). However, iKAT distinctively emphasizes
the creation and research of conversational search agents that adapt responses
based on user's prior interactions and present context. The challenge lies in
enabling Conversational Search Agents (CSA) to incorporate this personalized
context to efficiency and effectively guide users through the relevant
information to them. iKAT also emphasizes decisional search tasks, where users
sift through data and information to weigh up options in order to reach a
conclusion or perform an action. These tasks, prevalent in everyday
information-seeking decisions – be it related to travel, health, or shopping
– often revolve around a subset of high-level information operators where
queries or questions about the information space include: finding options,
comparing options, identifying the pros and cons of options, etc. Given the
different personas and their information need (expressed through the sequence
of questions), diverse conversation trajectories will arise – because the
answers to these similar queries will be very different. In this paper, we
report on the first year of TREC iKAT, describing the task, topics, data
collection, and evaluation framework. We further review the submissions and
summarize the findings.
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