Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
Conference of the European Chapter of the Association for Computational Linguistics(2024)
摘要
Clarifying questions are an integral component of modern information
retrieval systems, directly impacting user satisfaction and overall system
performance. Poorly formulated questions can lead to user frustration and
confusion, negatively affecting the system's performance. This research
addresses the urgent need to identify and leverage key features that contribute
to the classification of clarifying questions, enhancing user satisfaction. To
gain deeper insights into how different features influence user satisfaction,
we conduct a comprehensive analysis, considering a broad spectrum of lexical,
semantic, and statistical features, such as question length and sentiment
polarity. Our empirical results provide three main insights into the qualities
of effective query clarification: (1) specific questions are more effective
than generic ones; (2) the subjectivity and emotional tone of a question play a
role; and (3) shorter and more ambiguous queries benefit significantly from
clarification. Based on these insights, we implement feature-integrated user
satisfaction prediction using various classifiers, both traditional and
neural-based, including random forest, BERT, and large language models. Our
experiments show a consistent and significant improvement, particularly in
traditional classifiers, with a minimum performance boost of 45%. This study
presents invaluable guidelines for refining the formulation of clarifying
questions and enhancing both user satisfaction and system performance.
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