Sean MacAvaney
Franco Maria Nardini
Raffaele Perego
Nicola Tonellotto
Nazli Goharian
ABSTRACT
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.
- Nick Craswell, Bhaskar Mitra, and Daniel Campos. 2019. Overview of the TREC 2019 Deep Learning Track. In TREC.Google Scholar
- Zhuyun Dai and Jamie Callan. 2019. Context-aware sentence/passage term importance estimation for first stage retrieval. arXiv (2019).Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL.Google Scholar
- Laura Dietz, Ben Gamari, Jeff Dalton, and Nick Craswell. 2017. TREC Complex Answer Retrieval Overview. In TREC.Google Scholar
- Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016. A Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM.Google Scholar
- Helia Hashemi, Mohammad Aliannejadi, Hamed Zamani, and W. Bruce Croft. 2019. ANTIQUE: A Non-Factoid Question Answering Benchmark. arXiv (2019).Google Scholar
- Sebastian Hofst"atter and Allan Hanbury. 2019. Let's measure run time! Extending the IR replicability infrastructure to include performance aspects. In OSIRRC@SIGIR.Google Scholar
- Sebastian Hofst"atter, Markus Zlabinger, and Allan Hanbury. 2020. Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking. arXiv (2020).Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR.Google Scholar
- Sean MacAvaney. 2020. OpenNIR: A Complete Neural Ad-Hoc Ranking Pipeline. In WSDM.Google Scholar
- Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. 2019. CEDR: Contextualized Embeddings for Document Ranking. In SIGIR.Google ScholarDigital Library
- Bhaskar Mitra and Nick Craswell. 2019. An updated duet model for passage re-ranking. arXiv (2019).Google Scholar
- Rodrigo Nogueira and Kyunghyun Cho. 2019. Passage Re-ranking with BERT. arXiv (2019).Google Scholar
- Rodrigo Nogueira and Jimmy Lin. 2019. From doc2query to docTTTTTquery. (2019). https://cs.uwaterloo.ca/ jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdfGoogle Scholar
- Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019. Document expansion by query prediction. arXiv (2019).Google Scholar
- Colin Raffel, Noam Shazeer, Adam Kaleo Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. arXiv (2019).Google Scholar
- Nicola Tonellotto, Craig Macdonald, and Iadh Ounis. 2018. Efficient Query Processing for Scalable Web Search. Foundations and Trends in Information Retrieval, Vol. 12, 4--5 (2018), 319--492. http://dx.doi.org/10.1561/1500000057Google ScholarDigital Library
- Peilin Yang, Hui Fang, and Jimmy Lin. 2018. Anserini: Reproducible Ranking Baselines Using Lucene. J. Data and Information Quality, Vol. 10 (2018), 16:1--16:20.Google ScholarDigital Library
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