A Manifold Alignment Approach to Grounded Language Learning

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dc.contributor.authorRichards, Luke E.
dc.contributor.authorNguyen, Andre T.
dc.contributor.authorDarvish, Kasra
dc.contributor.authorRaff, Edward
dc.contributor.authorMatuszek, Cynthia
dc.date.accessioned2021-04-09T17:06:53Z
dc.date.available2021-04-09T17:06:53Z
dc.description.abstractAs robots become advanced and affordable enough to have in our daily lives, the next question is: How do we make using these machines as intuitive as possible? Language offers an approachable and relatively accessible interface without requiring prior training on the part of the user. We have seen the integration of voice-assistant speakers in homes drastically increase in the recent years. Voice, and more specifically language, is proving to be a preferred method for interacting with AI-enabled assistants.en_US
dc.format.extent2 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m2tx9b-5lly
dc.identifier.citationLuke E. Richards, Andre Nguyen, Kasra Darvish, Edward Raff, and Cynthia Matuszek. ´ “A Manifold Alignment Approach to Grounded Language Learning.” In the 8th Northeast Robotics Colloquium, http://iral.cs.umbc.edu/Pubs/RichardsNERC2019.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/21315
dc.language.isoen_USen_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.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleA Manifold Alignment Approach to Grounded Language Learningen_US
dc.typeTexten_US

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