Text REtrieval Conference (TREC) 2022¶

NeuCLIR¶

Cross-language Information Retrieval (CLIR) has been studied at TREC and subsequent evaluation forums for more than twenty years, but recent advances in the application of deep learning to information retrieval (IR) warrant a new, large-scale effort that will enable exploration of classical and modern IR techniques for this task.

Track coordinator(s):

Dawn Lawrie, Johns Hopkins University
Sean MacAvaney, University of Glasgow
James Mayfield, Johns Hopkins University
Paul McNamee, Johns Hopkins University
Douglas W. Oard, University of Maryland
Luca Soldaini, Amazon Alexa AI
Eugene Yang, Johns Hopkins University

Track Web Page: https://neuclir.github.io/

Health Misinformation¶

The Health Misinformation track aims to (1) provide a venue for research on retrieval methods that promote better decision making with search engines, and (2) develop new online and offline evaluation methods to predict the decision making quality induced by search results. Consumer health information is used as the domain of interest in the track.

Track coordinator(s):

Charlie Clarke, University of Waterloo
Maria Maistro, University of Copenhagen
Mark Smucker, University of Waterloo

Track Web Page: https://trec-health-misinfo.github.io/

Deep Learning¶

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

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

Conversational Assistance¶

The main aim of Conversational Assistance Track (CAsT) is to advance research on conversational search systems. The goal of the track is to create reusable benchmarks for open-domain information centric conversational dialogues.

Track coordinator(s):

Leif Azzopardi, University of Strathclyde
Jeff Dalton, University of Glasgow
Mohammed Alian Nejadi, University of Amsterdam
Paul Ogbonoko, University of Glasgow
Johanne Trippas, University of Melbourne
Svitlana Vakulenko, University of Amsterdam

Track Web Page: https://www.treccast.ai/

Clinical Trials¶

The goal of the Clinical Trials track is to focus research on the clinical trials matching problem: given a free text summary of a patient health record, find suitable clinical trials for that patient.

Track coordinator(s):

Dina Demner-Fushman, U.S. National Library of Medicine
William Hersh, Oregon Health and Science University
Kirk Roberts, University of Texas Health Science Center

Track Web Page: http://www.trec-cds.org/

Fair Ranking¶

Overview | Proceedings | Data | Runs | Participants

The Fair Ranking track focuses on building two-sided systems that offer fair exposure to ranked content producers while ensuring high results quality for ranking consumers.

Track coordinator(s):

Michael Ekstrand, Boise State University
Isaac Johnson, Wikimedia Foundation
Graham McDonald, University of Glasgow
Amifa Raj, Boise State University

Track Web Page: https://fair-trec.github.io/

CrisisFACTs¶

Overview | Proceedings | Data | Runs | Participants

The CrisisFACTS track focuses on temporal summarization for first responders in emergency situations. These summaries differ from traditional summarization in that they order information by time and produce a series of short updates instead of a longer narrative.

Track coordinator(s):

Cody Buntain, University of Maryland
Richard McCreadie, University of Glasgow

Track Web Page: https://crisisfacts.github.io/