Social-IQ

by Amir Zadeh1,Michael Chan1,Paul Pu Liang2,Edmund Tong1,Louis-Philippe Morency1 1 Language Technologies Institute,2 Machine Learning Department School of Computer Science,Carnegie Mellon UniversityUnknown

Social-IQ

Human language offers a unique unconstrained approach to probe through questions and reason through answers about social situations. This unconstrained approach extends previous attempts to model social intelligence through numeric supervision (e.g. sentiment and emotions labels). Social-IQ, is an unconstrained benchmark designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social-IQ contains 1,250 natural in-the-wild social situations, 7,500 questions and 52,500 correct and incorrect answers. Although humans can reason about social situations with very high accuracy (95.08%), existing state-of-the-art computational models struggle on this task.

Dataset Attributes

Label SVG
TasksImage Captioning
Label SVG
CategoriesSocial, Vqa, Text, Images, Morality, Morale
Label SVG
SensorRGB Camera