  Dense retrieval methods have shown great promise over sparse retrieval
methods in a range of NLP problems. Among them, dense phrase retrieval-the most
fine-grained retrieval unit-is appealing because phrases can be directly used
as the output for question answering and slot filling tasks. In this work, we
follow the intuition that retrieving phrases naturally entails retrieving
larger text blocks and study whether phrase retrieval can serve as the basis
for coarse-level retrieval including passages and documents. We first observe
that a dense phrase-retrieval system, without any retraining, already achieves
better passage retrieval accuracy (+3-5% in top-5 accuracy) compared to passage
retrievers, which also helps achieve superior end-to-end QA performance with
fewer passages. Then, we provide an interpretation for why phrase-level
supervision helps learn better fine-grained entailment compared to
passage-level supervision, and also show that phrase retrieval can be improved
to achieve competitive performance in document-retrieval tasks such as entity
linking and knowledge-grounded dialogue. Finally, we demonstrate how phrase
filtering and vector quantization can reduce the size of our index by 4-10x,
making dense phrase retrieval a practical and versatile solution in
multi-granularity retrieval.
