TY - GEN
T1 - Template-guided Clarifying Question Generation for Web Search Clarification
AU - Wang, Jian
AU - Li, Wenjie
N1 - Funding Information:
The work described in this paper was supported by Research Grants Council of Hong Kong (15207920, 15207821) and National Natural Science Foundation of China (62076212).
Publisher Copyright:
© 2021 ACM.
PY - 2021/10/26
Y1 - 2021/10/26
N2 - Clarification has attracted much attention because of its many potential applications especially in Web search. Since search queries are very short, the underlying user intents are often ambiguous. This makes it challenging for search engines to return the appropriate results that pertain to the users' actual information needs. To address this issue, asking clarifying questions has been recognized as a critical technique. Although previous studies have analyzed the importance of asking to clarify, generating clarifying questions for Web search remains under-explored. In this paper, we tackle this problem in a template-guided manner. Our objective is jointly learning to select question templates and fill question slots, using Transformer-based networks. We conduct experiments on MIMICS, a collection of datasets containing real Web search queries sampled from Bing's search logs. Our method is demonstrated to achieve significant improvements over various competitive baselines.
AB - Clarification has attracted much attention because of its many potential applications especially in Web search. Since search queries are very short, the underlying user intents are often ambiguous. This makes it challenging for search engines to return the appropriate results that pertain to the users' actual information needs. To address this issue, asking clarifying questions has been recognized as a critical technique. Although previous studies have analyzed the importance of asking to clarify, generating clarifying questions for Web search remains under-explored. In this paper, we tackle this problem in a template-guided manner. Our objective is jointly learning to select question templates and fill question slots, using Transformer-based networks. We conduct experiments on MIMICS, a collection of datasets containing real Web search queries sampled from Bing's search logs. Our method is demonstrated to achieve significant improvements over various competitive baselines.
KW - joint learning
KW - search clarification
KW - slot
KW - template
UR - http://www.scopus.com/inward/record.url?scp=85119203753&partnerID=8YFLogxK
U2 - 10.1145/3459637.3482199
DO - 10.1145/3459637.3482199
M3 - Conference article published in proceeding or book
AN - SCOPUS:85119203753
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 3468
EP - 3472
BT - CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Y2 - 1 November 2021 through 5 November 2021
ER -