Binary Acceleration and Compression for Dense Vector Entity Retrieval Models
WANG Yuanzheng1,2, FAN Yixing1,2, CHEN Wei1,2, ZHANG Ruqing1,2, GUO Jiafeng1,2
1. Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190; 2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408
Abstract：In entity retrieval tasks, dense vector entity retrieval models are utilized to efficiently filter candidate entities related to a query from a large-scale entity base.However, the existing dense vector retrieval models engender low real-time computation efficiency and large required storage space due to the high dimension of entity vectors. In this paper,it is found that these entity vectors contain a large amount of redundant information through experiments. Most entity vectors are distributed in non-overlapping quadrants and quadrants containing entities with similar semantics are also closer to each other.Thus, a binary entity retrieval method is proposed to compress entity vectors and accelerate similarity calculations.Specifically, the sign function is employed to binary-compress high-dimensional dense floating-point vectors, and Hamming distance is exploited to speed up the retrieval.The reason that the proposed method can guarantee the retrieval performance is theoretically analyzed.The correctness of the theory is verified through qualitative and quantitative analysis experiments, and a method for improving binary retrieval performance based on random dimension increase and rotation is provided.
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2023, Vol. 36 
