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An investigation of English-Irish machine translation and associated resources
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Dowling, Meghan. - : Dublin City University. School of Computing, 2022. : Dublin City University. ADAPT, 2022
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In: Dowling, Meghan orcid:0000-0003-1637-4923 (2022) An investigation of English-Irish machine translation and associated resources. PhD thesis, Dublin City University. (2022)
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A human evaluation of English-Irish statistical and neural machine translation
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Castilho, Sheila orcid:0000-0002-8416-6555 , Moorkens, Joss orcid:0000-0003-4864-5986 , Lynn, Teresa and Way, Andy orcid:0000-0001-5736-5930 (2020) A human evaluation of English-Irish statistical and neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 6 Nov 2020, Lisbon, Portugal. (2020)
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Morpho-syntactically annotated corpora provided for the PARSEME Shared Task on Semi-Supervised Identification of Verbal Multiword Expressions (edition 1.2)
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Leveraging backtranslation to improve machine translation for Gaelic language
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Lynn, Teresa and Way, Andy orcid:0000-0001-5736-5930 (2019) Leveraging backtranslation to improve machine translation for Gaelic language. In: Third Celtic Language Technology Workshop 2019, 19-23 Aug 2019, Dublin, Ireland. (2019)
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Adapting NMT to caption translation in Wikimedia Commons for low-resource languages
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Sarasola, Kepa orcid:0000-0003-4349-6088 , Dowling, Meghan orcid:0000-0003-1637-4923 , Way, Andy orcid:0000-0001-5736-5930 , Labaka, Gorka orcid:0000-0003-4611-2502 and Alegria, Iñaki orcid:0000-0002-0272-1472 (2019) Adapting NMT to caption translation in Wikimedia Commons for low-resource languages. Procesamiento de Lenguaje Natural, 63 . pp. 33-40. ISSN 1135-5948 (2019)
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Adapting NMT to caption translation in Wikimedia Commons for low-resource languages
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Sarasola, Kepa orcid:0000-0003-4349-6088 , Dowling, Meghan orcid:0000-0003-1637-4923 , Way, Andy orcid:0000-0001-5736-5930 , Labaka, Gorka orcid:0000-0003-4611-2502 and Alegria, Iñaki orcid:0000-0002-0272-1472 (2019) Adapting NMT to caption translation in Wikimedia Commons for low-resource languages. Procesamiento del Lenguaje Natural, 63 . pp. 33-40. ISSN 1135-5948 (2019)
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Investigating backtranslation for the improvement of English-Irish machine translation
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Lynn, Teresa and Way, Andy orcid:0000-0001-5736-5930 (2019) Investigating backtranslation for the improvement of English-Irish machine translation. Teanga, 26 . pp. 1-25. ISSN 0332-205X (2019)
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Adapting NMT to caption translation in Wikimedia Commons for low-resource languages ; Adaptando NMT a la traducción de pies de imagen en Wikimedia Commons para idiomas con pocos recursos
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Is all that glitters in MT quality estimation really gold standard?
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In: Graham, Yvette, Baldwin, Timothy, Dowling, Meghan orcid:0000-0003-1637-4923 , Eskevich, Maria, Lynn, Teresa and Tounsi, Lamia (2016) Is all that glitters in MT quality estimation really gold standard? In: 26th International Conference on Computational Linguistics, 11-17 Dec 2016, Osaka, Japan. ISBN 978-4-87974-702-0 (2016)
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Tapadoir: developing a statistical machine translation engine and associated resources for Irish
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Cassidy, Lauren, Maguire, Eimear, Lynn, Teresa, Srivastava, Ankit and Judge, John (2015) Tapadoir: developing a statistical machine translation engine and associated resources for Irish. In: 4th Biennial Workshop on Less-Resourced Languages (LRC 2015), 28 Nov 2015, Poznan, Poland. (2015)
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Abstract:
Tapadoir (from the Irish ´ tapa ‘fast’ and the nominal suffix -oir ´ ) is a statistical machine translation (SMT) project, funded by the Irish government. This work was commissioned to help government translators meet the translation demands which have arisen from the Irish language’s status as an official EU and national language. The development of this system, which translates English into Irish (a morphologically rich, low-resourced minority language), has produced an interesting set of challenges. These challenges have inspired a creative response to the lack of data and NLP tools available for the Irish language and have also resulted in the development of new resources for the Irish linguistic and NLP community. We show that our SMT system out-performs Google TranslateTM (a widely used general-domain SMT system) as a result of steps we have taken to tailor translation output to the user’s specific needs.
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URL: http://doras.dcu.ie/23605/
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