Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
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)
- 轨迹:
- The Future of Search & Recommendation
- 日期：
- 演讲者：
- Chenyan Xiong
- 所属机构：
- Microsoft Research Redmond
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Chenyan Xiong
Principal Researcher
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The Future of Search & Recommendation
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Keynote: Universal search and recommendation
Speakers:- Paul Bennett
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Research talk: Learning and pretraining strategies for dense retrieval in search and beyond
Speakers:- Chenyan Xiong
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Research talk: Is phrase retrieval all we need?
Speakers:- Danqi Chen
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Research talk: IGLU: Interactive grounded language understanding in a collaborative environment
Speakers:- Julia Kiseleva
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Research talk: Summarizing information across multiple documents and modalities
Speakers:- Subhojit Som
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Panel: The future of search and recommendation: Beyond web search
Speakers:- Eric Horvitz,
- Nitin Agrawal,
- Soumen Chakrabati
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Research talk: Attentive knowledge-aware graph neural networks for recommendation
Speakers:- Yaming Yang
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Panel: Causality in search and recommendation systems
Speakers:- Emre Kiciman,
- Amit Sharma,
- Dean Eckles
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