Compact Token Representations with Contextual Quantization for Efficient Document Re-ranking

TL;DR

This paper introduces a novel method for compressing token representations in transformer-based document re-ranking models using contextual quantization, which improves search relevance while reducing storage and computational costs.

Contribution

It proposes a decoupled codebook-based compression technique for token embeddings that enhances online decompression and relevance in document re-ranking.

Findings

Improved relevance in document re-ranking tasks.

Reduced storage requirements for token representations.

Maintained or improved search accuracy with compression.

Abstract

Transformer based re-ranking models can achieve high search relevance through context-aware soft matching of query tokens with document tokens. To alleviate runtime complexity of such inference, previous work has adopted a late interaction architecture with pre-computed contextual token representations at the cost of a large online storage. This paper proposes contextual quantization of token embeddings by decoupling document-specific and document-independent ranking contributions during codebook-based compression. This allows effective online decompression and embedding composition for better search relevance. This paper presents an evaluation of the above compact token representation model in terms of relevance and space efficiency.