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arXiv · 2107.13602

Domain-matched Pre-training Tasks for Dense Retrieval

Barlas Oğuz·Kushal Lakhotia·Anchit Gupta·Patrick Lewis·Vladimir Karpukhin·Aleksandra Piktus·Xilun Chen·Sebastian Riedel·Wen-tau Yih·Sonal Gupta·Yashar Mehdad
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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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Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad. 2021-07-28. Domain-matched Pre-training Tasks for Dense Retrieval. https://arxiv.org/abs/2107.13602

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