Domain-matched Pre-training Tasks for Dense Retrieval

• URL: http://arxiv.org/abs/2107.13602v1
• Date: Wed, 28 Jul 2021 19:13:00 GMT
• Title: Domain-matched Pre-training Tasks for Dense Retrieval
• Authors: Barlas O\u{g}uz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad
• Abstract summary: Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations.
• Score: 68.07140087626637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. A notable exception is information retrieval, where additional pre-training has so far failed to produce convincing results. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically 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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