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Expansion via Prediction of Importance with Contextualization

SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Page: 1573-1576
2020
  • 65
    Citations
  • 0
    Usage
  • 28
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    65
    • Citation Indexes
      65
  • Captures
    28
  • Mentions
    1
    • References
      1
      • 1

Conference Paper Description

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.

Bibliographic Details

Sean MacAvaney; Nazli Goharian; Ophir Frieder; Franco Maria Nardini; Raffaele Perego; Nicola Tonellotto

Association for Computing Machinery (ACM)

Computer Science

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