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