User Intent Prediction in Information-seeking Conversations

Qu, C, Yang, L, Croft, W, Zhang, Y, Trippas, J and Qiu, M 2019, 'User Intent Prediction in Information-seeking Conversations', in Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, Glasgow, Scotland UK, 10-14 March 2019, pp. 25-33.


Document type: Conference Paper
Collection: Conference Papers

Title User Intent Prediction in Information-seeking Conversations
Author(s) Qu, C
Yang, L
Croft, W
Zhang, Y
Trippas, J
Qiu, M
Year 2019
Conference name ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR)
Conference location Glasgow, Scotland UK
Conference dates 10-14 March 2019
Proceedings title Proceedings of the 2019 Conference on Human Information Interaction and Retrieval
Publisher ACM
Place of publication United States
Start page 25
End page 33
Total pages 9
Abstract onversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.
Subjects Information Retrieval and Web Search
Keyword(s) User Intent Prediction
Information-seeking Conversations
Conversational Search
Multi-turn Question Answering
DOI - identifier 10.1145/3295750.3298924
Copyright notice © 2019 Association for Computing Machinery
ISBN 9781450360258
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