Leading Conversational Search by Suggesting Useful Questions
Corbin Rosset, Chenyan Xiong, Xia Song, Daniel Campos, Nick Craswell, Saurabh Tiwary, Paul N. Bennett
Abstract
This paper studies a new scenario in conversational search, conversational question suggestion, which leads search engine users to more engaging experiences by suggesting interesting, informative, and useful follow-up questions. We first establish a novel evaluation metric, usefulness, which goes beyond relevance and measures whether the suggestions provide valuable information for the next step of a user’s journey, and construct a public benchmark for useful question suggestion. Then we develop two suggestion systems, a BERT based ranker and a GPT-2 based generator, both trained with novel weak supervision signals that convey past users’ search behaviors in search sessions. The weak supervision signals help ground the suggestions to users’ information-seeking trajectories: we identify more coherent and informative sessions using encodings, and then weakly supervise our models to imitate how users transition to the next state of search. Our offline experiments demonstrate the crucial role our “next-turn” inductive training plays in improving usefulness over a strong online system. Our online A/B test in Bing shows that our more useful question suggestions receive 8% more user clicks than the previous system.