  Efficient document retrieval heavily relies on the technique of semantic
hashing, which learns a binary code for every document and employs Hamming
distance to evaluate document distances. However, existing semantic hashing
methods are mostly established on outdated TFIDF features, which obviously do
not contain lots of important semantic information about documents.
Furthermore, the Hamming distance can only be equal to one of several integer
values, significantly limiting its representational ability for document
distances. To address these issues, in this paper, we propose to leverage BERT
embeddings to perform efficient retrieval based on the product quantization
technique, which will assign for every document a real-valued codeword from the
codebook, instead of a binary code as in semantic hashing. Specifically, we
first transform the original BERT embeddings via a learnable mapping and feed
the transformed embedding into a probabilistic product quantization module to
output the assigned codeword. The refining and quantizing modules can be
optimized in an end-to-end manner by minimizing the probabilistic contrastive
loss. A mutual information maximization based method is further proposed to
improve the representativeness of codewords, so that documents can be quantized
more accurately. Extensive experiments conducted on three benchmarks
demonstrate that our proposed method significantly outperforms current
state-of-the-art baselines.
