2026-09-18T21:52:18Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1180362022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Boris Katz.
Ross, Candace Cheronda
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-09-17T15:54:46Z
2018-09-17T15:54:46Z
2018
2018
http://hdl.handle.net/1721.1/118036
1051458939
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 45-47).
We develop a semantic parser which is trained in a grounded setting using pairs of videos captioned with sentences. This setting is both data-efficient requiring little annotation and far more similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences and despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. We introduce a new corpus for grounded language acquisition. Learning to understand language, turn sentences into logical forms, by using captioned video will significantly expand the range of data that parsers can be trained on, lower the effort of training a semantic parser, and ultimately lead to a better understanding of child language acquisition.
by Candace Cheronda Ross.
S.M.
47 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Grounded semantic parsing using captioned videos
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="538aa1f5-5f29-4240-a4da-2c6e37788ed3">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Grounded semantic parsing using captioned videos</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2018</PublicationDate>
<Authors>
<Author>
<DisplayName>Ross, Candace Cheronda</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>We develop a semantic parser which is trained in a grounded setting using pairs of videos captioned with sentences. This setting is both data-efficient requiring little annotation and far more similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences and despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. We introduce a new corpus for grounded language acquisition. Learning to understand language, turn sentences into logical forms, by using captioned video will significantly expand the range of data that parsers can be trained on, lower the effort of training a semantic parser, and ultimately lead to a better understanding of child language acquisition.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>