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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 What level of quality can neural machine translation attain on literary text? Toral, Antonio; Way, Andy In: Toral, Antonio orcid:0000-0003-2357-2960 and Way, Andy orcid:0000-0001-5736-5930 (2018) What level of quality can neural machine translation attain on literary text? In: Moorkens, Joss orcid:0000-0003-4864-5986 , Castilho, Sheila orcid:0000-0002-8416-6555 , Gaspari, Federico orcid:0000-0003-3808-8418 and Doherty, S, (eds.) Translation Quality Assessment: From Principles to Practice. Machine Translation: Technologies and Applications book series (MATRA), 1 . Springer, Berlin/Heidelberg, 263 -287. ISBN 978-3-319-91240-0 (2018) BASE Show details 2 Improving machine translation of educational content via crowdsourcing Behnke, Maximiliana; Miceli Barone, Antonio Valerio; Sennrich, Rico; Sosoni, Vilelmini; Naskos, Thanasis; Takoulidou, Eirini; Stasimioti, Maria; Menno, van Zaanen; Castilho, Sheila; Gaspari, Federico; Georgakopoulou, Panayota; Kordoni, Valia; Egg, Markus; Kermanidis, Katia Lida In: Behnke, Maximiliana, Miceli Barone, Antonio Valerio, Sennrich, Rico, Sosoni, Vilelmini, Naskos, Thanasis, Takoulidou, Eirini, Stasimioti, Maria, Menno, van Zaanen, Castilho, Sheila orcid:0000-0002-8416-6555 , Gaspari, Federico orcid:0000-0003-3808-8418 , Georgakopoulou, Panayota orcid:0000-0001-9780-1813 , Kordoni, Valia, Egg, Markus and Kermanidis, Katia Lida orcid:0000-0002-3270-5078 (2018) Improving machine translation of educational content via crowdsourcing. In: LREC 2018 - 11th International Conference on Language Resources and Evaluation, Miyazaki, Japan. ISBN 979-10-95546-19-1 (2018) Abstract: The limited availability of in-domain training data is a major issue in the training of application-specific neural machine translation models. Professional outsourcing of bilingual data collections is costly and often not feasible. In this paper we analyze the influence of using crowdsourcing as a scalable way to obtain translations of target in-domain data having in mind that the translations can be of a lower quality. We apply crowdsourcing with carefully designed quality controls to create parallel corpora for the educational domain by collecting translations of texts from MOOCs from English to eleven languages, which we then use to fine-tune neural machine translation models previously trained on general-domain data. The results from our research indicate that crowdsourced data collected with proper quality controls consistently yields performance gains over general-domain baseline systems, and systems fine-tuned with pre-existing in-domain corpora. Keyword: crowdsourcing; Machine translating; MOOCs; neural machine translation URL: http://doras.dcu.ie/23201/ BASE Hide details 3 Reading comprehension of machine translation output: what makes for a better read? Castilho, Sheila; Guerberof Arenas, Ana In: Castilho, Sheila orcid:0000-0002-8416-6555 and Guerberof Arenas, Ana orcid:0000-0001-9820-7074 (2018) Reading comprehension of machine translation output: what makes for a better read? In: 21st Annual Conference of the European for Machine Translation, 28-30 May 2018, Alacant/Alicante, Spain. ISBN 978-84-09-01901-4 (2018) BASE Show details 4 Attaining the unattainable? Reassessing claims of human parity in neural machine translation Toral, Antonio; Hu, Ke; Way, Andy... In: Toral, Antonio orcid:0000-0003-2357-2960 , Castilho, Sheila orcid:0000-0002-8416-6555 , Hu, Ke and Way, Andy orcid:0000-0001-5736-5930 (2018) Attaining the unattainable? Reassessing claims of human parity in neural machine translation. In: Third Conference on Machine Translation (WMT), 31 Oct- 1 Nov 2018, Brussels, Belgium. ISBN 978-1-948087-81-0 (2018) BASE Show details

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