Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset
This work addresses the need for better social reasoning assessments in AI and human studies, such as for children with autism, but is incremental as it builds on existing social VQA methodologies.
The paper tackles the problem of characterizing social visual question answering (VQA) datasets to address variations in videos and questions, resulting in the creation of the TinySocial dataset and new rubrics for difficulty and content analysis.
Modern social intelligence includes the ability to watch videos and answer questions about social and theory-of-mind-related content, e.g., for a scene in Harry Potter, "Is the father really upset about the boys flying the car?" Social visual question answering (social VQA) is emerging as a valuable methodology for studying social reasoning in both humans (e.g., children with autism) and AI agents. However, this problem space spans enormous variations in both videos and questions. We discuss methods for creating and characterizing social VQA datasets, including 1) crowdsourcing versus in-house authoring, including sample comparisons of two new datasets that we created (TinySocial-Crowd and TinySocial-InHouse) and the previously existing Social-IQ dataset; 2) a new rubric for characterizing the difficulty and content of a given video; and 3) a new rubric for characterizing question types. We close by describing how having well-characterized social VQA datasets will enhance the explainability of AI agents and can also inform assessments and educational interventions for people.