Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset [Konferenzbericht] Paper presented at the IEEE Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL/EPIROB) (10th, Valparaiso, Chile, Oct 26-30, 2020).

Autor/inn/en	Chen, Zhanwen; Li, Shiyao; Rashedi, Roxanne; Zi, Xiaoman; Elrod-Erickson, Morgan; Hollis, Bryan; Maliakal, Angela; Shen, Xinyu; Zhao, Simeng; Kunda, Maithilee
Titel	Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset [Konferenzbericht] Paper presented at the IEEE Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL/EPIROB) (10th, Valparaiso, Chile, Oct 26-30, 2020).
Quelle	(2020), (6 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Visual Stimuli; Questioning Techniques; Social Cognition; Video Technology; Scoring Rubrics; Artificial Intelligence; Theory of Mind; Data Collection; Heuristics; Films; Middle School Students; Autism Spectrum Disorders + Suchen Sie Ihr Suchwort? Befragungstechnik; Fragetechnik; Soziale Kognition; Scoring formulas; Auswertungsbogen; Künstliche Intelligenz; Data capture; Datensammlung; Heuristik; Film; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Autism; Autismus
Abstract	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. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01