The identification of relevance with little textual context is a primary
challenge in passage retrieval. We address this problem with a
representation-based ranking approach that: (1) explicitly models the
importance of each term using a contextualized language model; (2) performs
passage expansion by propagating the importance to similar terms; and (3)
grounds the representations in the lexicon, making them interpretable. Passage
representations can be pre-computed at index time to reduce query-time latency.
We call our approach EPIC (Expansion via Prediction of Importance with
Contextualization). We show that EPIC significantly outperforms prior
importance-modeling and document expansion approaches. We also observe that the
performance is additive with the current leading first-stage retrieval methods,
further narrowing the gap between inexpensive and cost-prohibitive passage
ranking approaches. Specifically, EPIC achieves a MRR@10 of 0.304 on the
MS-MARCO passage ranking dataset with 78ms average query latency on commodity
hardware. We also find that the latency is further reduced to 68ms by pruning
document representations, with virtually no difference in effectiveness.

本文提出了一种通过基于上下文的语言模型来显式建模每个词汇的重要性、扩展查询方法以及基于词典对表示的理解性进行打分的 EPIC 方法，该方法在 MRR@10 上达到了 0.304，且能够显著提高检索效果和降低硬件的查询延迟。