  The embedding-based retrieval (EBR) approach is widely used in mainstream
search engine retrieval systems and is crucial in recent retrieval-augmented
methods for eliminating LLM illusions. However, existing EBR models often face
the "semantic drift" problem and insufficient focus on key information, leading
to a low adoption rate of retrieval results in subsequent steps. This issue is
especially noticeable in real-time search scenarios, where the various
expressions of popular events on the Internet make real-time retrieval heavily
reliant on crucial event information. To tackle this problem, this paper
proposes a novel approach called EER, which enhances real-time retrieval
performance by improving the dual-encoder model of traditional EBR. We
incorporate contrastive learning to accompany pairwise learning for encoder
optimization. Furthermore, to strengthen the focus on critical event
information in events, we include a decoder module after the document encoder,
introduce a generative event triplet extraction scheme based on prompt-tuning,
and correlate the events with query encoder optimization through comparative
learning. This decoder module can be removed during inference. Extensive
experiments demonstrate that EER can significantly improve the real-time search
retrieval performance. We believe that this approach will provide new
perspectives in the field of information retrieval. The codes and dataset are
available at https://github.com/open-event-hub/Event-enhanced_Retrieval .
