  A vital step towards the widespread adoption of neural retrieval models is
their resource efficiency throughout the training, indexing and query
workflows. The neural IR community made great advancements in training
effective dual-encoder dense retrieval (DR) models recently. A dense text
retrieval model uses a single vector representation per query and passage to
score a match, which enables low-latency first stage retrieval with a nearest
neighbor search. Increasingly common, training approaches require enormous
compute power, as they either conduct negative passage sampling out of a
continuously updating refreshing index or require very large batch sizes for
in-batch negative sampling. Instead of relying on more compute capability, we
introduce an efficient topic-aware query and balanced margin sampling
technique, called TAS-Balanced. We cluster queries once before training and
sample queries out of a cluster per batch. We train our lightweight 6-layer DR
model with a novel dual-teacher supervision that combines pairwise and in-batch
negative teachers. Our method is trainable on a single consumer-grade GPU in
under 48 hours (as opposed to a common configuration of 8x V100s). We show that
our TAS-Balanced training method achieves state-of-the-art low-latency (64ms
per query) results on two TREC Deep Learning Track query sets. Evaluated on
NDCG@10, we outperform BM25 by 44%, a plainly trained DR by 19%, docT5query by
11%, and the previous best DR model by 5%. Additionally, TAS-Balanced produces
the first dense retriever that outperforms every other method on recall at any
cutoff on TREC-DL and allows more resource intensive re-ranking models to
operate on fewer passages to improve results further.
