Roundtable discussion: Beyond language models: Knowledge, multiple modalities, and more
- Yonatan Bisk, Daniel McDuff, Dragomir Radev | Carnegie Mellon University, Microsoft Research Redmond, Yale University
- Microsoft Research Summit 2021 | Deep Learning & Large-Scale AI
In this roundtable discussion, we will discuss the current state-of-the-art in language models (LMs) and how the lack of external and commonsense knowledge is a limiting factor in several applications, including open domain question answering. This additional information or knowledge could come from structured databases, knowledge graphs, images or other modalities, and we will cover some novel approaches for incorporating this data. Our discussion will cover modeling, but also the available datasets that can help to drive advances in this domain.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
Deep Learning & Large-Scale AI
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Opening remarks: Deep Learning and Large-Scale AI
- Ahmed Awadallah
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Roundtable discussion: Efficient and adaptable large-scale AI
- Ahmed Awadallah,
- Jianfeng Gao,
- Danqi Chen
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Panel: Large-scale neural platform models: Opportunities, concerns, and directions
- Eric Horvitz,
- Miles Brundage,
- Yejin Choi
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Research talk: WebQA: Multihop and multimodal
- Yonatan Bisk
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Roundtable discussion: Beyond language models: Knowledge, multiple modalities, and more
- Yonatan Bisk,
- Daniel McDuff,
- Dragomir Radev
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Closing remarks: Deep Learning and Large Scale AI
- Jianfeng Gao
