  Product quantization (PQ) is a widely used technique for ad-hoc retrieval.
Recent studies propose supervised PQ, where the embedding and quantization
models can be jointly trained with supervised learning. However, there is a
lack of appropriate formulation of the joint training objective; thus, the
improvements over previous non-supervised baselines are limited in reality. In
this work, we propose the Matching-oriented Product Quantization (MoPQ), where
a novel objective Multinoulli Contrastive Loss (MCL) is formulated. With the
minimization of MCL, we are able to maximize the matching probability of query
and ground-truth key, which contributes to the optimal retrieval accuracy.
Given that the exact computation of MCL is intractable due to the demand of
vast contrastive samples, we further propose the Differentiable Cross-device
Sampling (DCS), which significantly augments the contrastive samples for
precise approximation of MCL. We conduct extensive experimental studies on four
real-world datasets, whose results verify the effectiveness of MoPQ. The code
is available at https://github.com/microsoft/MoPQ.

Computation and Language;Information Retrieval