Generative document retrieval, an emerging paradigm in information retrieval,
learns to build connections between documents and identifiers within a single
model, garnering significant attention. However, there are still two
challenges: (1) neglecting inner-content correlation during document
representation; (2) lacking explicit semantic structure during identifier
construction. Nonetheless, events have enriched relations and well-defined
taxonomy, which could facilitate addressing the above two challenges. Inspired
by this, we propose Event GDR, an event-centric generative document retrieval
model, integrating event knowledge into this task. Specifically, we utilize an
exchange-then-reflection method based on multi-agents for event knowledge
extraction. For document representation, we employ events and relations to
model the document to guarantee the comprehensiveness and inner-content
correlation. For identifier construction, we map the events to well-defined
event taxonomy to construct the identifiers with explicit semantic structure.
Our method achieves significant improvement over the baselines on two datasets,
and also hopes to provide insights for future research.

提出了 Event GDR，一种以事件为中心的生成式文档检索模型，通过将事件知识集成到任务中，解决了忽略文档内部内容相关性和标识符构建缺乏明确语义结构的两个挑战。通过多代理的交换 - 反射方法来进行事件知识提取，利用事件和关系对文档进行建模以保证全面性和内部内容相关性，将事件映射到明确的事件分类法构建标识符。实验证明，该方法在两个数据集上明显优于基线，并希望为未来的研究提供启示。