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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 – 5 of 5 1 Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Esplà-Gomis, Miquel; Pérez-Ortiz, Juan Antonio. - : Underline Science Inc., 2021 BASE Show details 2 Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio. - : Association for Computational Linguistics, 2021 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) Abstract: This paper reports on a pilot experiment that compares two different machine translation (MT) paradigms in reading comprehension tests. To explore a suitable methodology, we set up a pilot experiment with a group of six users (with English, Spanish and Simplified Chinese languages) using an English Language Testing System (IELTS), and an eye-tracker. The users were asked to read three texts in their native language: either the original English text (for the English speakers) or the machine-translated text (for the Spanish and Simplified Chinese speakers). The original texts were machine-translated via two MT systems: neural (NMT) and statistical (SMT). The users were also asked to rank satisfaction statements on a 3-point scale after reading each text and answering the respective comprehension questions. After all tasks were completed, a post-task retrospective interview took place to gather qualitative data. The findings suggest that the users from the target languages completed more tasks in less time with a higher level of satisfaction when using translations from the NMT system. Keyword: Machine translating URL: http://doras.dcu.ie/23071/ BASE Hide details 4 Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries Forcada, Mikel L.; Carrasco, Rafael C.; Pérez-Ortiz, Juan Antonio. - : Springer Science+Business Media Dordrecht, 2017 BASE Show details 5 Choosing the correct paradigm for unknown words in rule-based machine translation systems Sánchez-Cartagena, Víctor M.; Esplà-Gomis, Miquel; Sánchez-Martínez, Felipe. - : CSLI Publications, 2012 BASE Show details

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