Conference paper Open Access
Pham, MinhQuang; Crego, Josep; Yvon, François; 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.3524979"> <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.3524979</dct:identifier> <foaf:page rdf:resource="https://doi.org/10.5281/zenodo.3524979"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Pham, MinhQuang</foaf:name> <foaf:givenName>MinhQuang</foaf:givenName> <foaf:familyName>Pham</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>SYSTRAN / 5 rue Feydeau, 75002 Paris, France & LIMSI, CNRS, Université Paris-Saclay 91405 Orsay, 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>Yvon, François</foaf:name> <foaf:givenName>François</foaf:givenName> <foaf:familyName>Yvon</foaf:familyName> <org:memberOf> <foaf:Organization> <foaf:name>LIMSI, CNRS, Université Paris-Saclay 91405 Orsay, 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>Generic and Specialized Word Embeddings for Multi-Domain 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> <owl:sameAs rdf:resource="https://zenodo.org/record/3524979"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/3524979</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:isVersionOf rdf:resource="https://doi.org/10.5281/zenodo.3524978"/> <dct:isPartOf rdf:resource="https://zenodo.org/communities/iwslt2019"/> <dct:description><p>Supervised machine translation works well when the train and test data are sampled from the same distribution. When this is not the case,&nbsp;adaptation&nbsp;techniques help ensure that the knowledge learned from out-of-domain texts generalises to in-domain sentences. We study here a related setting,&nbsp;multi-domain adaptation, where the number of domains is potentially large and adapting separately to each domain would waste training resources. Our proposal transposes to neural machine translation the feature expansion technique of (Daum&eacute;&nbsp;III, 2007): it isolates domain-agnostic from domain-specific lexical representations, while sharing the most of the network across domains. Our experiments use two architectures and two language pairs: they show that our approach, while simple and computationally inexpensive, outperforms several strong baselines and delivers a multi-domain system that successfully translates texts from diverse sources.</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.3524979"/> </dcat:Distribution> </dcat:distribution> <dcat:distribution> <dcat:Distribution> <dcat:accessURL>https://doi.org/10.5281/zenodo.3524979</dcat:accessURL> <dcat:byteSize>336061</dcat:byteSize> <dcat:downloadURL>https://zenodo.org/record/3524979/files/IWSLT2019_paper_10.pdf</dcat:downloadURL> <dcat:mediaType>application/pdf</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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