Research talk: Search, summarization, and sensemaking
Natural language processing (NLP) has undergone head-spinning advances over the last 5–10 years. At the same time, user interfaces for search have remained somewhat static. Has NLP advanced enough to more actively aid searchers in the sensemaking process? In this talk, Professor Marti Hearst of UC Berkeley summarizes some recent work by her lab, some in collaboration with Microsoft Research, on integration of summarization and question answering into news chat interfaces. She will also discuss other recent developments of integrating NLP into scholarly document search and discuss how this might lead to more powerful support for making sense of information within and across search results.
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：
- Marti Hearst
- 所属机构：
- UC Berkeley
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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