Publication:
Craft: a benchmark for causal reasoning about forces and in teractions

dc.contributor.coauthorAteş, Tayfun
dc.contributor.coauthorAteşoğlu, M. Şamil
dc.contributor.coauthorYiğit, Çağatay
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Psychology
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.departmentDepartment of Psychology
dc.contributor.kuauthorErdem, Aykut
dc.contributor.kuauthorGöksun, Tilbe
dc.contributor.kuauthorYüret, Deniz
dc.contributor.kuauthorKesen, İlker
dc.contributor.kuauthorKobaş, Mert
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofileFaculty Member
dc.contributor.kuprofileMaster Student
dc.contributor.researchcenterKoç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI)
dc.contributor.schoolcollegeinstituteGraduate School of Sciences and Engineering
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteCollege of Social Sciences and Humanities
dc.contributor.yokid20331
dc.contributor.yokid47278
dc.contributor.yokid179996
dc.contributor.yokidN/A
dc.contributor.yokidN/A
dc.contributor.yokidN/A
dc.date.accessioned2024-11-09T13:53:09Z
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.fulltextYES
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipCRAFT 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.description.versionPublisher version
dc.formatpdf
dc.identifier.doi10.18653/v1/2022.findings-acl.205
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR04026
dc.identifier.isbn9781955917254
dc.identifier.linkhttps://doi.org/10.18653/v1/2022.findings-acl.205
dc.identifier.quartileN/A
dc.identifier.urihttps://hdl.handle.net/20.500.14288/4004
dc.keywordsArtificial intelligence
dc.keywordsTheory and methods
dc.languageEnglish
dc.publisherAssociation for Computational Linguistics (ACL)
dc.relation.grantnoNA
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10944
dc.sourceFindings of the Association for Computational Linguistics
dc.subjectComputer science
dc.subjectLinguistics
dc.titleCraft: a benchmark for causal reasoning about forces and in teractions
dc.typeConference proceeding
dspace.entity.typePublication
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local.contributor.authorid0000-0002-7039-0046
local.contributor.authoridN/A
local.contributor.authoridN/A
local.contributor.authoridN/A
local.contributor.kuauthorErdem, Aykut
local.contributor.kuauthorGöksun, Tilbe
local.contributor.kuauthorYüret, Deniz
local.contributor.kuauthorKesen, İlker
local.contributor.kuauthorKobaş, Mert
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