148

Applications in Natural Language Processing

TABLE 5.8

Quantization results of Bi-ColBERT.

Model

MRR@10

BERTbase

16.7

BERTlarge

19.8

ColBERT

32.8

Bi-ColBERT

31.7

In summary, this paper’s contributions can be concluded as: (1) The first work to binarize

ColBERT. (2) A semantic diffusion method to hedge the information loss against embedding

binarization. (3) An approximation of Unit Impulse Function [18] for more accurate gradient

estimation.