The data is available at our Git repository: https://github.com/kj2013/claff-happydb/
Submission Site: https://easychair.org/conferences/conference_dir.cgi?a=19604803
Everyone who participates, gets to present at least a poster and have their paper in our CEUR-WS proceedings.
Full CFPs: https://sites.google.com/view/affcon2019/home
Invited speakers: Ellen Riloff (University of Utah), Alon Halevy (Megagon Labs), Lyle Ungar (University of Pennsylvania), Pranav Anand (University of Santa Cruz)
GIVEN: An account of a happy moment, marked with individual's demographics, recollection time and relevant labels.
TASK 1: WHAT ARE THE INGREDIENTS FOR HAPPINESS ?
Semi-supervised learning task: Predict agency and social labels for happy moments in the test set, based on a small labeled and large unlabeled training data.
TASK 2: HOW CAN WE MODEL HAPPINESS ?
Unsupervised task: Propose new characterizations and insights (not necessarily and not limited to themes) for happy moments in the test set, e.g., in terms of affect, emotion, participants and content.
Aug 25, 2018 - Training set released
Nov 1, 2018 - Test set released
>>>>>November 5, 2018 - Abstract submission with a short system description*
November 30, 2018 - System runs due & AAAI conference early registration deadline
December 5, 2018 - System reports (full papers) due and peer review process begins
TBD - Camera-ready contributions
*(prefix your submission title with "[CL-Aff Shared Task]")
Niyati Chhaya (Adobe Research, nchhaya at adobe.com),
Kokil Jaidka (University of Pennsylvania / Nanyang Technological University, kokil.j at gmail.com ),
Lyle Ungar (University of Pennsylvania, ungar at cis.upenn.edu),
Atanu R Sinha (Adobe Research, atr at adobe.com)
Shared Task Data contributed by Megagon Labs
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