Publication:
CRAFT: a benchmark for causal reasoning about forces and inTeractions

dc.contributor.coauthorAtes, Tayfun
dc.contributor.coauthorAtesoglu, M. Samil
dc.contributor.coauthorYigit, Cagatay
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Psychology
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.departmentGraduate School of Social Sciences and Humanities
dc.contributor.kuauthorErdem, Aykut
dc.contributor.kuauthorGöksun, Tilbe
dc.contributor.kuauthorKesen, İlker
dc.contributor.kuauthorKobaş, Mert
dc.contributor.kuauthorYüret, Deniz
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteCollege of Social Sciences and Humanities
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SOCIAL SCIENCES AND HUMANITIES
dc.date.accessioned2024-11-09T23:14:20Z
dc.date.issued2022
dc.description.abstractHumans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT1, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessNO
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipTurkish Academy of Sciences
dc.description.sponsorshipBAGEP 2021 Award of the Science Academy
dc.description.sponsorshipKUIS AI Center This work was supported in part by GEBIP 2018 Award of the Turkish Academy of Sciences to E. Erdem and T. Goksun, BAGEP 2021 Award of the Science Academy to A. Erdem, and AI Fellowship to Ilker Kesen provided by the KUIS AI Center.
dc.identifier.isbn978-1-955917-25-4
dc.identifier.scopus2-s2.0-85149143498
dc.identifier.urihttps://hdl.handle.net/20.500.14288/10142
dc.identifier.wos828767402053
dc.keywordsInferences
dc.keywordsMotion
dc.language.isoeng
dc.publisherAssoc Computational Linguistics-Acl
dc.relation.ispartofFindings Of The Association For Computational Linguistics (Acl 2022)
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectComputer science
dc.subjectLinguistics
dc.titleCRAFT: a benchmark for causal reasoning about forces and inTeractions
dc.typeConference Proceeding
dspace.entity.typePublication
local.contributor.kuauthorKesen, İlker
local.contributor.kuauthorKobaş, Mert
local.contributor.kuauthorErdem, Erkut
local.contributor.kuauthorErdem, Aykut
local.contributor.kuauthorGöksun, Tilbe
local.contributor.kuauthorYüret, Deniz
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1GRADUATE SCHOOL OF SOCIAL SCIENCES AND HUMANITIES
local.publication.orgunit1College of Engineering
local.publication.orgunit1College of Social Sciences and Humanities
local.publication.orgunit2Department of Computer Engineering
local.publication.orgunit2Department of Psychology
local.publication.orgunit2Graduate School of Sciences and Engineering
local.publication.orgunit2Graduate School of Social Sciences and Humanities
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