Text REtrieval Conference (TREC) 2021¶

Incident Streams¶

Overview | Proceedings | Data | Runs | Participants

The Incident Streams track is designed to bring together academia and industry to research technologies to automatically process social media streams during emergency situations with the aim of categorizing information and aid requests made on social media for emergency service operators.

Track coordinator(s):

Cody Buntain, New Jersey Institute of Technology
Richard McCreadie, University of Glasgow
Ian Soboroff, National Institute of Standards and Technology (NIST)

Track Web Page: https://www.dcs.gla.ac.uk/~richardm/TREC_IS/

News¶

The News track features modern search tasks in the news domain. In partnership with The Washington Post, the track develops test collections that support the search needs of news readers and news writers in the current news environment.

Track coordinator(s):

Donna Harman, National Institute of Standards and Technology (NIST)
Shudong Huang, National Institute of Standards and Technology (NIST)
Ian Soboroff, National Institute of Standards and Technology (NIST)

Track Web Page: http://trec-news.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
Graham McDonald, University of Glasgow
Amifa Raj, Boise State University

Track Web Page: https://fair-trec.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
Nick Craswell, Microsoft
Jimmy Lin, Microsoft
Bhaskar Mitra, Microsoft
Emine Yilmaz, University College London

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

Clinical Trials¶

The goal of the new 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
Ellen Voorhees, National Institute of Standards and Technology (NIST)

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

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):

Jamie Callan, Carnegie Mellon University
Jeff Dalton, University of Glasgow
Chenyan Xiong, Microsoft Research

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

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/

Podcast¶

The aim of the Podcasts track is to develop methods for information retrieval and content understanding from open-domain podcast transcripts and audio.

Track coordinator(s):

Ann Clifton, Spotify
Ben Carterette, Spotify
Maria Eskevich, CLARIN ERIC
Gareth Jones, Dublin City University
Rosie Jones, Spotify
Jussi Karlgren, Spotify
Sravana Reddy, Spotify
Md Iftekhar Tanveer, Spotify

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