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dc.contributor.authorChakravarthi, Bharathi Raja
dc.contributor.authorPriyadharshini, Ruba
dc.contributor.authorStearns, Bernardo
dc.contributor.authorJayapal, Arun
dc.contributor.authorSridevy, S.
dc.contributor.authorArcan, Mihael
dc.contributor.authorZarrouk, Manel
dc.contributor.authorMcCrae, John P.
dc.date.accessioned2019-09-10T11:12:28Z
dc.date.available2019-09-10T11:12:28Z
dc.date.issued2019-08-19
dc.identifier.citationChakravarthi, Bharathi Raja, Priyadharshini, Ruba, Stearns, Bernardo, Jayapal, Arun, Sridevy, S., Arcan, Mihael, Zarrouk, Manel, McCrae, John P. (2019). Multilingual multimodal machine translation for Dravidian languages utilizing phonetic transcription. Paper presented at the LoResMT 2019 : 2nd Workshop on Technologies for MT of Low Resource Languages (LoResMT 2019 at MT Summit XVII), Dublin, Ireland, 19-23 August.en_IE
dc.identifier.urihttp://hdl.handle.net/10379/15415
dc.description.abstractMultimodal machine translation is the task of translating from a source text into the target language using information from other modalities. Existing multimodal datasets have been restricted to only highly resourced languages. In addition to that, these datasets were collected by manual translation of English descriptions from the Flickr30K dataset. In this work, we introduce MMDravi, a Multilingual Multimodal dataset for under-resourced Dravidian languages. It comprises of 30,000 sentences which were created utilizing several machine translation outputs. Using data from MMDravi and a phonetic transcription of the corpus, we build an Multilingual Multimodal Neural Machine Translation system (MMNMT) for closely related Dravidian languages to take advantage of multilingual corpus and other modalities. We evaluate our translations generated by the proposed approach with human-annotated evaluation dataset in terms of BLEU, METEOR, and TER metrics. Relying on multilingual corpora, phonetic transcription, and image features, our approach improves the translation quality for the underresourced languages.en_IE
dc.description.sponsorshipThis work is supported by a research grant from Science Foundation Ireland, co-funded by the European Regional Development Fund, for the Insight Centre under Grant Number SFI/12/RC/2289 and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731015, ELEXIS - European Lexical Infrastructure and grant agreement No 825182, Pret- ˆ a-` LLOD.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherEuropean Association for Machine Translationen_IE
dc.relation.ispartofProceedings of the 2nd Workshop on Technologies for MT of Low Resource Languagesen
dc.subjectMachine translationen_IE
dc.subjectDravidian languagesen_IE
dc.subjectPhonetic transcriptionen_IE
dc.titleMultilingual multimodal machine translation for Dravidian languages utilizing phonetic transcriptionen_IE
dc.typeWorkshop paperen_IE
dc.date.updated2019-08-29T08:13:58Z
dc.local.publishedsourcehttps://www.mtsummit2019.com/workshopsen_IE
dc.description.peer-reviewednon-peer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderEuropean Regional Development Funden_IE
dc.contributor.funderHorizon 2020en_IE
dc.internal.rssid17436610
dc.local.contactBharathi Raja Asoka Chakravarthi, Insight Centre For Data Analytics, National University Of Ireland Galway . Email: b.asokachakravarthi1@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionACCEPTED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/731015/EU/European Lexicographic Infrastructure/ELEXISen_IE
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/825182/EU/Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors/Pret-a-LLODen_IE
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