Social Commonsense-Guided Search Query Generation for Open-Domain
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Revanth Gangi Reddy Hao Bai Wentao Yao Sharath Chandra Etagi Suresh Heng Ji ChengXiang Zhai
Open-domain dialog involves generating search queries that help obtain
relevant knowledge for holding informative conversations. However, it can be
challenging to determine what information to retrieve when the user is passive
and does not express a clear need or request. To tackle this issue, we present
a novel approach that focuses on generating internet search queries that are
guided by social commonsense. Specifically, we leverage a commonsense dialog
system to establish connections related to the conversation topic, which
subsequently guides our query generation. Our proposed framework addresses
passive user interactions by integrating topic tracking, commonsense response
generation and instruction-driven query generation. Through extensive
evaluations, we show that our approach overcomes limitations of existing query
generation techniques that rely solely on explicit dialog information, and
produces search queries that are more relevant, specific, and compelling,
ultimately resulting in more engaging responses.