Expansion via Prediction of Importance with Contextualization
This work addresses the problem of efficient and accurate passage retrieval for information retrieval systems, showing incremental improvements by combining with existing methods.
The paper tackles the challenge of passage retrieval with limited textual context by introducing EPIC, a representation-based ranking approach that models term importance and expands passages, achieving an MRR@10 of 0.304 on the MS-MARCO dataset with 78ms query latency.
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.