Overview - Deep Learning 2022

Proceedings | Data | Results | Runs | Participants

The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.

Track coordinator(s):

• Daniel Campos, University of Illinois at Urbana-Champaign
• Nick Craswell, Microsoft
• Jimmy Lin, University of Waterloo
• Bhaskar Mitra, Microsoft
• Emine Yilmaz, University College London

Tasks:

• docs: Document Ranking
• passages: Passage Ranking

Track Web Page: https://microsoft.github.io/msmarco/TREC-Deep-Learning

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