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)
- トラック:
- The Future of Search & Recommendation
- 日付:
- スピーカー:
- Chenyan Xiong
- 所属:
- Microsoft Research Redmond
The Future of Search & Recommendation
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Keynote: Universal search and recommendation
Speakers:- Paul Bennett
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Keynote: Extreme classification for dense retrieval and personalized recommendation
Speakers:- Manik Varma
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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: Extracting pragmatics from content interactions to improve enterprise recommendations
Speakers:- Jennifer Neville
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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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Closing remarks: The Future of Search and Recommendation
Speakers:- Susan Dumais
