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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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Abstract:
Human-targeted metrics provide a compromise between human evaluation of machine translation, where high inter-annotator agreement is difficult to achieve, and fully automatic metrics, such as BLEU or TER, that lack the validity of human assessment. Human-targeted translation edit rate (HTER) is by far the most widely employed human-targeted metric in machine translation, commonly employed, for example, as a gold standard in evaluation of quality estimation. Original experiments justifying the design of HTER, as opposed to other possible formulations, were limited to a small sample of translations and a single language pair, however, and this motivates our re-evaluation of a range of human-targeted metrics on a substantially larger scale. Results show significantly stronger correlation with human judgment for HBLEU over HTER for two of the nine language pairs we include and no significant difference between correlations achieved by HTER and HBLEU for the remaining language pairs. Finally, we evaluate a range of quality estimation systems employing HTER and direct assessment (DA) of translation adequacy as gold labels, resulting in a divergence in system rankings, and propose employment of DA for future quality estimation evaluations.
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Keyword:
Machine translating
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URL: http://doras.dcu.ie/23608/
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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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