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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) Abstract: Given the rise of the new neural approach to machine translation (NMT) and its promising performance on different text types, we assess the translation quality it can attain on what is perceived to be the greatest challenge for MT: literary text. Specifically, we target novels, arguably the most popular type of literary text. We build a literary-adapted NMT system for the English-to-Catalan translation direction and evaluate it against a system pertaining to the previous dominant paradigm in MT: statistical phrase-based MT (PBSMT). To this end, for the first time we train MT systems, both NMT and PBSMT, on large amounts of literary text (over 100 million words) and evaluate them on a set of 12 widely known novels spanning from the 1920s to the present day. According to the BLEU automatic evaluation metric, NMT is significantly better than PBSMT (p Keyword: Literature translation; Machine translating; Neural machine translation; Pairwise ranking; Phrase-based statistical machine translation URL: http://doras.dcu.ie/24599/ BASE Hide details 2 Improving machine translation of educational content via crowdsourcing Behnke, Maximiliana; Miceli Barone, Antonio Valerio; Sennrich, Rico... 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) BASE Show 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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