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From bilingual to multilingual neural-based machine translation by incremental training
dc.contributor.author | Escolano Peinado, Carlos |
dc.contributor.author | Ruiz Costa-Jussà, Marta |
dc.contributor.author | Rodríguez Fonollosa, José Adrián |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2021-03-10T14:08:07Z |
dc.date.available | 2021-03-10T14:08:07Z |
dc.date.issued | 2020-08-02 |
dc.identifier.citation | Escolano, C.; Costa-jussà, M.R.; Fonollosa, J.A.R. From bilingual to multilingual neural-based machine translation by incremental training. "Journal of the Association for Information Science and Technology", 2 Agost 2020, vol. 72, núm. 2, p. 190-203. |
dc.identifier.issn | 2330-1643 |
dc.identifier.uri | http://hdl.handle.net/2117/341408 |
dc.description.abstract | A common intermediate language representation in neural machine translation can be used to extend bilingual systems by incremental training. We propose a new architecture based on introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we can train multiple encoders and decoders for each language, sharing among them a common intermediate representation. Translation results on the low-resource tasks (Turkish-English and Kazakh-English tasks) show a BLEU improvement of up to 2.8 points. However, results on a larger dataset (Russian-English and Kazakh-English) show BLEU losses of a similar amount. While our system provides improvements only for the low-resource tasks in terms of translation quality, our system is capable of quickly deploying new language pairs without the need to retrain the rest of the system, which may be a game changer in some situations. Specifically, what is most relevant regarding our architecture is that it is capable of: reducing the number of production systems, with respect to the number of languages, from quadratic to linear; incrementally adding a new language to the system without retraining the languages already there; and allowing for translations from the new language to all the others present in the system. |
dc.description.sponsorship | This work also is supported in part by the Google Faculty Research Awards and, also by, the Spanish Ministerio de Economía y Competitividad, the European Regional Development Fund, the Agencia Estatal de Investigación through the postdoctoral senior grant Ramón y Cajal and the projects EUR2019-103819, PCIN-2017-079 andPID2019-107579RB-I00. |
dc.format.extent | 14 p. |
dc.language.iso | eng |
dc.rights | Attribution 4.0 International |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Llenguatge natural |
dc.subject.lcsh | Computational linguistics |
dc.subject.lcsh | Machine translating |
dc.subject.other | Architecture-based |
dc.subject.other | Bilingual systems |
dc.subject.other | Encoders and decoders |
dc.subject.other | Incremental training |
dc.subject.other | Intermediate languages |
dc.subject.other | Intermediate representations |
dc.title | From bilingual to multilingual neural-based machine translation by incremental training |
dc.type | Article |
dc.subject.lemac | Lingüística computacional |
dc.subject.lemac | Traducció automàtica |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1002/asi.24395 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://asistdl.onlinelibrary.wiley.com/doi/full/10.1002/asi.24395 |
dc.rights.access | Open Access |
local.identifier.drac | 29194499 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación (PEICTI) 2013-2016/PCIN-2017-079/ES/AUTONOMOUS LIFELONG LEARNING INTELLIGENT SYSTEMS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/2PE/EUR2019-103819 |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107579RB-I00/ES/ARQUITECTURAS AVANZADAS DE APRENDIZAJE PROFUNDO APLICADAS AL PROCESADO DE VOZ, AUDIO Y LENGUAJE/ |
local.citation.author | Escolano, C.; Costa-jussà, M. R.; Fonollosa, José A. R. |
local.citation.publicationName | Journal of the Association for Information Science and Technology |
local.citation.volume | 72 |
local.citation.number | 2 |
local.citation.startingPage | 190 |
local.citation.endingPage | 203 |
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