Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
- Chenyan Xiong | Microsoft Research Redmond
- Microsoft Research Summit 2021 | The Future of Search & Recommendation
In this talk, we’ll quickly go through our recent observations and findings with dense retrieval. First, we’ll recap the standard setup and training strategies for dense retrieval models in search. We’ll then share our recent progress on generalizing dense retrieval to recommendation, complex question answering, and latent reasoning. Finally, we’ll discuss the challenges with mismatches between pretrained models and dense retrieval requirements and our early attempts in dense retrieval dedicated pretraining techniques.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
The Future of Search & Recommendation
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Keynote: Universal search and recommendation
- Paul Bennett
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Research talk: System frontiers for dense retrieval
- Jason Li,
- Knut Risvik
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Research talk: Domain-specific pretraining for vertical search
- Tristan Naumann
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Panel: The future of search and recommendation: Beyond web search
- Eric Horvitz,
- Nitin Agrawal,
- Soumen Chakrabati
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Panel: Causality in search and recommendation systems
- Emre Kiciman,
- Amit Sharma,
- Dean Eckles
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