@inproceedings{oguz-etal-2022-domain,
title = "Domain-matched Pre-training Tasks for Dense Retrieval",
author = "Oguz, Barlas and
Lakhotia, Kushal and
Gupta, Anchit and
Lewis, Patrick and
Karpukhin, Vladimir and
Piktus, Aleksandra and
Chen, Xilun and
Riedel, Sebastian and
Yih, Scott and
Gupta, Sonal and
Mehdad, Yashar",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.114",
doi = "10.18653/v1/2022.findings-naacl.114",
pages = "1524--1534",
abstract = "Pre-training on larger datasets with ever increasing model size isnow a proven recipe for increased performance across almost all NLP tasks.A notable exception is information retrieval, where additional pre-traininghas so far failed to produce convincing results. We show that, with theright pre-training setup, this barrier can be overcome. We demonstrate thisby pre-training large bi-encoder models on 1) a recently released set of 65 millionsynthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations made available by pushshift.io. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines.",
}
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<abstract>Pre-training on larger datasets with ever increasing model size isnow a proven recipe for increased performance across almost all NLP tasks.A notable exception is information retrieval, where additional pre-traininghas so far failed to produce convincing results. We show that, with theright pre-training setup, this barrier can be overcome. We demonstrate thisby pre-training large bi-encoder models on 1) a recently released set of 65 millionsynthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations made available by pushshift.io. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines.</abstract>
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%0 Conference Proceedings
%T Domain-matched Pre-training Tasks for Dense Retrieval
%A Oguz, Barlas
%A Lakhotia, Kushal
%A Gupta, Anchit
%A Lewis, Patrick
%A Karpukhin, Vladimir
%A Piktus, Aleksandra
%A Chen, Xilun
%A Riedel, Sebastian
%A Yih, Scott
%A Gupta, Sonal
%A Mehdad, Yashar
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F oguz-etal-2022-domain
%X Pre-training on larger datasets with ever increasing model size isnow a proven recipe for increased performance across almost all NLP tasks.A notable exception is information retrieval, where additional pre-traininghas so far failed to produce convincing results. We show that, with theright pre-training setup, this barrier can be overcome. We demonstrate thisby pre-training large bi-encoder models on 1) a recently released set of 65 millionsynthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations made available by pushshift.io. We evaluate on a set of information retrieval and dialogue retrieval benchmarks, showing substantial improvements over supervised baselines.
%R 10.18653/v1/2022.findings-naacl.114
%U https://aclanthology.org/2022.findings-naacl.114
%U https://doi.org/10.18653/v1/2022.findings-naacl.114
%P 1524-1534
Markdown (Informal)
[Domain-matched Pre-training Tasks for Dense Retrieval](https://aclanthology.org/2022.findings-naacl.114) (Oguz et al., Findings 2022)
ACL
- Barlas Oguz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Scott Yih, Sonal Gupta, and Yashar Mehdad. 2022. Domain-matched Pre-training Tasks for Dense Retrieval. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1524–1534, Seattle, United States. Association for Computational Linguistics.