[Corpora-List] Last CFP! Happiness Shared Task @ AAAI AffCon : Modeling Affect-in-Action

[#KOKIL JAIDKA#]

You are invited to participate in the first CL-Aff Shared Task, to be held as a part of the Affective Content Analysis workshop @ AAAI 2019. In the quest to understand user expression, we propose a task focusing on one facet of human affect - happiness. We contribute a new labeled corpus of happy moments and pose two novel challenges to spur the development of supervised and semi-supervised approaches to model human affect.

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)

Tasks

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.

Upcoming deadlines

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]")

Co-chairs:

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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