In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning. Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects. The task is to answer questions from a collection of cooking recipes and videos, where each question belongs to a “question family” reflecting a specific reasoning competence. The data and task result is publicly available.
SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering / Tu, Jingxuan; Holderness, Eben; Maru, Marco; Conia, Simone; Rim, Kyeongmin; Lynch, Kelley; Brutti, Richard; Navigli, Roberto; Pustejovsky, James. - (2022), pp. 1244-1255. (Intervento presentato al convegno 16th International Workshop on Semantic Evaluation (SemEval-2022) tenutosi a Seattle; United States) [10.18653/v1/2022.semeval-1.176].
SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering
Maru, Marco;Conia, Simone;Navigli, Roberto
;Pustejovsky, James
2022
Abstract
In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning. Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects. The task is to answer questions from a collection of cooking recipes and videos, where each question belongs to a “question family” reflecting a specific reasoning competence. The data and task result is publicly available.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
