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 purpose of the CL-Aff Shared Task is to challenge the current understanding of emotion analysis through a task that models the experiential, contextual and agentic attributes of happy moments. It has long been known that human affect is context-driven, and that labeled datasets should account for these factors in generating predictive models of affect. The Shared Task is organized in collaboration with researchers at Megagon Labs and builds upon the HappyDB dataset, comprising human accounts of 'happy moments'. The Shared Task comprises two sub-tasks for analyzing happiness and wellbeing in written language, on a corpus of 100,000 descriptions of happy moments.
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.Shared Task Submission Deadlines: Submissions via EasyChair (prefix your submission title with "[CL-Aff Shared Task]")
January 27/28: All participants present at the Workshop at AAAI-19
Contact Kokil Jaidka (jaidka at sas.upenn.edu) or Niyati Chhaya (nchhaya at adobe.com) and we promise to help.