Hierarchical Corpus Encoder: Fusing Generative Retrieval and Dense Indices

Tongfei Chen, Ankita Sharma, Adam Pauls, Benjamin Van Durme

Keywords: information retrieval, generative retrieval, dense retrieval

TL;DR: We propose hierarchical corpus encoder, a document retriever that jointly learns a dense retriever and a document hierarchy.

Abstract: Generative retrieval employs sequence models for conditional generation of document IDs based on a query (DSI (Tay et al. (2022); NCI (Wang et al., 2022); inter alia). While this has led to improved performance in zero-shot retrieval, it is a challenge to support documents not seen during training. We identify the performance of generative retrieval lies in contrastive training between sibling nodes in a document hierarchy. This motivates our proposal, the _hierarchical corpus encoder_ (HCE), which can be supported by traditional dense encoders. Our experiments show that HCE achieves superior results than generative retrieval models under both unsupervised zero-shot and supervised settings, while also allowing the easy addition and removal of documents to the index.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 2713