Research talk: Is phrase retrieval all we need?
DensePhrases is an extractive phrase-search tool based on natural language input that achieves dense retrieval of billion-scale phrases with extreme runtime efficiency. In this talk, Assistant Professor Danqi Chen of Princeton University will highlight some of the technical challenges that she and her research team encountered and the solutions of learning dense representations of phrases at scale. She’ll demonstrate the strong performance on open-domain QA and slot-filling tasks, and she’ll show how phrase retrieval, the most fine-grained retrieval unit, can also be used for passage or document retrieval tasks. Finally, she’ll cover how phrase filtering and vector quantization can make the phrase index much smaller, making dense phrase retrieval a practical and versatile solution in multi-granularity retrieval.
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：
- Danqi Chen
- 所属机构：
- Princeton University
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Danqi Chen
Assistant Professor
Princeton University
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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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