Research talk: Domain-specific pretraining for vertical search
Information overload is a prevalent challenge in many high-value domains. Search in biomedicine, and many other vertical domains, is challenging due to the scarcity of direct supervision from click logs. Self-supervised learning has emerged as a promising direction to overcome the annotation bottleneck. In this talk, we discuss work that presents a general approach for vertical search based on domain-specific pretraining and present a case study in the biomedical domain, Microsoft Biomedical Search.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- The Future of Search & Recommendation
- Date:
- Speakers:
- Tristan Naumann
- Affiliation:
- Microsoft Research
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Tristan Naumann
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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