Event GDR: Event-Centric Generative Document Retrieval
Yong Guan, Dingxiao Liu, Jinchen Ma, Hao Peng, Xiaozhi Wang, Lei Hou,, Ru Li

TL;DR
Event GDR introduces an event-centric approach to generative document retrieval, leveraging event knowledge and taxonomy to improve document representation and identifier construction, resulting in significant performance gains.
Contribution
This paper presents a novel event-centric model that incorporates event knowledge and taxonomy into generative document retrieval, addressing key challenges in content correlation and semantic structure.
Findings
Significant improvement over baselines on two datasets
Effective integration of event knowledge enhances document representation
Explicit semantic structure in identifiers benefits retrieval accuracy
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSemantic Web and Ontologies · Data Quality and Management · Natural Language Processing Techniques
