Semi-Parametric Retrieval via Binary Token Index

• URL: http://arxiv.org/abs/2405.01924v1
• Date: Fri, 3 May 2024 08:34:13 GMT
• Title: Semi-Parametric Retrieval via Binary Token Index
• Authors: Jiawei Zhou, Li Dong, Furu Wei, Lei Chen,
• Abstract summary: Semi-parametric Vocabulary Disentangled Retrieval (SVDR) is a novel semi-parametric retrieval framework. It supports two types of indexes: an embedding-based index for high effectiveness, akin to existing neural retrieval methods; and a binary token index that allows for quick and cost-effective setup, resembling traditional term-based retrieval. It achieves a 3% higher top-1 retrieval accuracy compared to the dense retriever DPR when using an embedding-based index and a 9% higher top-1 accuracy compared to BM25 when using a binary token index.
• Score: 71.78109794895065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The landscape of information retrieval has broadened from search services to a critical component in various advanced applications, where indexing efficiency, cost-effectiveness, and freshness are increasingly important yet remain less explored. To address these demands, we introduce Semi-parametric Vocabulary Disentangled Retrieval (SVDR). SVDR is a novel semi-parametric retrieval framework that supports two types of indexes: an embedding-based index for high effectiveness, akin to existing neural retrieval methods; and a binary token index that allows for quick and cost-effective setup, resembling traditional term-based retrieval. In our evaluation on three open-domain question answering benchmarks with the entire Wikipedia as the retrieval corpus, SVDR consistently demonstrates superiority. It achieves a 3% higher top-1 retrieval accuracy compared to the dense retriever DPR when using an embedding-based index and an 9% higher top-1 accuracy compared to BM25 when using a binary token index. Specifically, the adoption of a binary token index reduces index preparation time from 30 GPU hours to just 2 CPU hours and storage size from 31 GB to 2 GB, achieving a 90% reduction compared to an embedding-based index.

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