Deep neural networks based on gating mechanism for open-domain question answering
Descripción del Articulo
I would like to thank in a special way the National Council of Science, Technology and Technological Innovation (CONCYTEC) and the National Fund for Scientific, Technological development and Technological Innovation (FONDECYT-CIENCIACTIVA), which through the Management Agreement N 234-2015-FONDECYT,...
| Autor: | |
|---|---|
| Formato: | tesis de maestría |
| Fecha de Publicación: | 2018 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/1731 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/1731 |
| Nivel de acceso: | acceso abierto |
| Materia: | Question Answering Machine Comprehension Natural Language Processing Deep Learning https://purl.org/pe-repo/ocde/ford#1.02.01 |
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CONC_78de6ecc75c926f221fbdc91dd3b4a8a |
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oai:repositorio.concytec.gob.pe:20.500.12390/1731 |
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CONC |
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CONCYTEC-Institucional |
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4689 |
| dc.title.none.fl_str_mv |
Deep neural networks based on gating mechanism for open-domain question answering |
| title |
Deep neural networks based on gating mechanism for open-domain question answering |
| spellingShingle |
Deep neural networks based on gating mechanism for open-domain question answering Arch Tijera, Drake Christian Question Answering Machine Comprehension Natural Language Processing Processing Deep Learning https://purl.org/pe-repo/ocde/ford#1.02.01 |
| title_short |
Deep neural networks based on gating mechanism for open-domain question answering |
| title_full |
Deep neural networks based on gating mechanism for open-domain question answering |
| title_fullStr |
Deep neural networks based on gating mechanism for open-domain question answering |
| title_full_unstemmed |
Deep neural networks based on gating mechanism for open-domain question answering |
| title_sort |
Deep neural networks based on gating mechanism for open-domain question answering |
| author |
Arch Tijera, Drake Christian |
| author_facet |
Arch Tijera, Drake Christian |
| author_role |
author |
| dc.contributor.author.fl_str_mv |
Arch Tijera, Drake Christian |
| dc.subject.none.fl_str_mv |
Question Answering |
| topic |
Question Answering Machine Comprehension Natural Language Processing Processing Deep Learning https://purl.org/pe-repo/ocde/ford#1.02.01 |
| dc.subject.es_PE.fl_str_mv |
Machine Comprehension Natural Language Processing Processing Deep Learning |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#1.02.01 |
| description |
I would like to thank in a special way the National Council of Science, Technology and Technological Innovation (CONCYTEC) and the National Fund for Scientific, Technological development and Technological Innovation (FONDECYT-CIENCIACTIVA), which through the Management Agreement N 234-2015-FONDECYT, they have allowed the grant and financing of my studies of Master in Computer Science at the Universidad Cat´olica San Pablo (UCSP). |
| publishDate |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2018 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| format |
masterThesis |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/1731 |
| url |
https://hdl.handle.net/20.500.12390/1731 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
| dc.publisher.none.fl_str_mv |
Universidad Católica San Pablo |
| publisher.none.fl_str_mv |
Universidad Católica San Pablo |
| dc.source.none.fl_str_mv |
reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
| instname_str |
Consejo Nacional de Ciencia Tecnología e Innovación |
| instacron_str |
CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
| collection |
CONCYTEC-Institucional |
| repository.name.fl_str_mv |
Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
| _version_ |
1844883047063748608 |
| spelling |
Publicationrp04663600Arch Tijera, Drake Christian2024-05-30T23:13:38Z2024-05-30T23:13:38Z2018https://hdl.handle.net/20.500.12390/1731I would like to thank in a special way the National Council of Science, Technology and Technological Innovation (CONCYTEC) and the National Fund for Scientific, Technological development and Technological Innovation (FONDECYT-CIENCIACTIVA), which through the Management Agreement N 234-2015-FONDECYT, they have allowed the grant and financing of my studies of Master in Computer Science at the Universidad Cat´olica San Pablo (UCSP).Nowadays, Question Answering is being addressed from a reading comprehension approach. Usually, Machine Comprehension models are poweredby Deep Learning algorithms. Most related work faces the challenge by improving the Interaction Encoder, proposing several architectures strongly based on attention. In Contrast, few related work has focused on improving the Context Encoder. Thus, our work has explored in depth the Context Encoder. We propose a gating mechanism that controls the ow of information, from the Context Encoder towards Interaction Encoder. This gating mechanism is based on additional information computed previously. Our experiments has shown that our proposed model improved the performance of a competitive baseline model. Our single model reached 78.36% on F1 score and 69.1% on exact match metric, on the Stanford Question Answering benchmark.Consejo Nacional de Ciencia, Tecnología e InnovaciónengUniversidad Católica San Pabloinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/Question AnsweringMachine Comprehension-1Natural Language-1Processing-1Processing-1Deep Learning-1https://purl.org/pe-repo/ocde/ford#1.02.01-1Deep neural networks based on gating mechanism for open-domain question answeringinfo:eu-repo/semantics/masterThesisreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/1731oai:repositorio.concytec.gob.pe:20.500.12390/17312024-05-30 15:39:41.635https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f1fc8de3-eb11-44fb-a525-1634614d0c31"> <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>Deep neural networks based on gating mechanism for open-domain question answering</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Arch Tijera, Drake Christian</DisplayName> <Person id="rp04663" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Universidad Católica San Pablo</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/licenses/by/4.0/</License> <Keyword>Question Answering</Keyword> <Keyword>Machine Comprehension</Keyword> <Keyword>Natural Language</Keyword> <Keyword>Processing</Keyword> <Keyword>Processing</Keyword> <Keyword>Deep Learning</Keyword> <Abstract>Nowadays, Question Answering is being addressed from a reading comprehension approach. Usually, Machine Comprehension models are poweredby Deep Learning algorithms. Most related work faces the challenge by improving the Interaction Encoder, proposing several architectures strongly based on attention. In Contrast, few related work has focused on improving the Context Encoder. Thus, our work has explored in depth the Context Encoder. We propose a gating mechanism that controls the ow of information, from the Context Encoder towards Interaction Encoder. This gating mechanism is based on additional information computed previously. Our experiments has shown that our proposed model improved the performance of a competitive baseline model. Our single model reached 78.36% on F1 score and 69.1% on exact match metric, on the Stanford Question Answering benchmark.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
| score |
13.277489 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
