Conference paper Open Access
Xu, Jitao; Crego, Josep; Senellart, Jean
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://doi.org/10.5281/zenodo.3524977"> <rdf:type rdf:resource="http://www.w3.org/ns/dcat#Dataset"/> <dct:type rdf:resource="http://purl.org/dc/dcmitype/Text"/> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://doi.org/10.5281/zenodo.3524977</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.3524977"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Xu, Jitao</foaf:name> <foaf:givenName>Jitao</foaf:givenName> <foaf:familyName>Xu</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>SYSTRAN, 5 rue Feydeau, 75002 Paris (France)</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Crego, Josep</foaf:name> <foaf:givenName>Josep</foaf:givenName> <foaf:familyName>Crego</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>SYSTRAN, 5 rue Feydeau, 75002 Paris (France)</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Senellart, Jean</foaf:name> <foaf:givenName>Jean</foaf:givenName> <foaf:familyName>Senellart</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>SYSTRAN, 5 rue Feydeau, 75002 Paris (France)</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:title>Lexical Micro-adaptation for Neural Machine Translation</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-11-02</dct:issued> <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/> <owl:sameAs rdf:resource="https://zenodo.org/record/3524977"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3524977</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.3524976"/> <dct:isPartOf rdf:resource="https://zenodo.org/communities/iwslt2019"/> <dct:description><p>This work is inspired by a typical machine translation industry scenario in which translators make use of in-domain data for facilitating translation of similar or repeating sentences. We introduce a generic framework applied at inference in which a subset of segment pairs are first extracted from training data according to their similarity to the input sentences. These segments are then used to dynamically update the parameters of a generic NMT network, thus performing a&nbsp;lexical micro-adaptation. Our approach demonstrates strong adaptation performance to new and existing datasets including pseudo in-domain data. We evaluate our approach on a heterogeneous English-French training dataset showing accuracy gains on all evaluated domains when compared to strong adaptation baselines.</p></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dcat:distribution> <dcat:Distribution> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:accessURL rdf:resource="https://doi.org/10.5281/zenodo.3524977"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL>https://doi.org/10.5281/zenodo.3524977</dcat:accessURL> <dcat:byteSize>432333</dcat:byteSize> <dcat:downloadURL>https://zenodo.org/record/3524977/files/IWSLT2019_paper_9.pdf</dcat:downloadURL> <dcat:mediaType>application/pdf</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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