  Sponsored search is a key revenue source for search engines, where
advertisers bid on keywords to target users or search queries of interest.
However, finding relevant keywords for a given query is challenging due to the
large and dynamic keyword space, ambiguous user/advertiser intents, and diverse
possible topics and languages. In this work, we present a comprehensive
comparison between two paradigms for online query rewriting: Generative (NLG)
and Dense Retrieval (DR) methods. We observe that both methods offer
complementary benefits that are additive. As a result, we show that around 40%
of the high-quality keywords retrieved by the two approaches are unique and not
retrieved by the other. To leverage the strengths of both methods, we propose
CLOVER-Unity, a novel approach that unifies generative and dense retrieval
methods in one single model. Through offline experiments, we show that the NLG
and DR components of CLOVER-Unity consistently outperform individually trained
NLG and DR models on public and internal benchmarks. Furthermore, we show that
CLOVER-Unity achieves 9.8% higher good keyword density than the ensemble of two
separate DR and NLG models while reducing computational costs by almost half.
We conduct extensive online A/B experiments on Microsoft Bing in 140+ countries
and achieve improved user engagement, with an average increase in total clicks
by 0.89% and increased revenue by 1.27%. We also share our practical lessons
and optimization tricks for deploying such unified models in production.
