Unsupervised Selection of Negative Examples for Grounded Language Learning

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dc.contributor.authorPillai, Nisha
dc.contributor.authorMatuszek, Cynthia
dc.date.accessioned2018-09-05T20:56:56Z
dc.date.available2018-09-05T20:56:56Z
dc.date.issued2018-02
dc.descriptionThirty-Second AAAI Conference on Artificial Intelligence, 2018en_US
dc.description.abstractThere has been substantial work in recent years on grounded language acquisition, in which language and sensor data are used to create a model relating linguistic constructs to the perceivable world. While powerful, this approach is frequently hindered by ambiguities, redundancies, and omissions found in natural language. We describe an unsupervised system that learns language by training visual classifiers, first selecting important terms from object descriptions, then automatically choosing negative examples from a paired corpus of perceptual and linguistic data. We evaluate the effectiveness of each stage as well as the system's performance on the overall learning task.en_US
dc.description.sponsorshipThis material is based upon work supported by the National Science Foundation under Grant No. 1657469.en_US
dc.description.urihttps://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17440en_US
dc.format.extent9 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M2WD3Q48B
dc.identifier.citationNisha Pillai, Cynthia Matuszek, Unsupervised Selection of Negative Examples for Grounded Language Learning, Thirty-Second AAAI Conference on Artificial Intelligence , 2018, https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17440en_US
dc.identifier.urihttp://hdl.handle.net/11603/11241
dc.language.isoen_USen_US
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)en_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectroboticsen_US
dc.subjecthuman-robot-interactionen_US
dc.subjectnatural-language-processingen_US
dc.subjectInteractive Robotics and Language Laben_US
dc.titleUnsupervised Selection of Negative Examples for Grounded Language Learningen_US
dc.typeTexten_US

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