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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 Page: 1 2 3 Hits 1 – 20 of 56 1 MultiTraiNMT: training materials to approach neural machine translation from scratch ; MultiTraiNMT: des outils pour se former à la traduction automatique neuronale Ramírez-Sánchez, Gema; Antonio Pérez-Ortiz, Juan; Sánchez-Martínez, Felipe... In: TRITON 2021 (Translation and Interpreting Technology Online) ; https://hal.archives-ouvertes.fr/hal-03272570 ; TRITON 2021 (Translation and Interpreting Technology Online), Jul 2021, Online, United Kingdom (2021) BASE Show details 2 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 3 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 4 Fuzzy-Match Repair in Computer-Aided Translation Using Black-Box Machine Translation Ortega, John E.. - : Universidad de Alicante, 2021 BASE Show details 5 Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation ... The 28th International Conference on Computational Linguistics 2020; Pérez-Ortiz, Juan Antonio; Sánchez-Cartagena, Víctor Manuel. - : Underline Science Inc., 2020 BASE Show details 6 Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : Association for Computational Linguistics, 2020 BASE Show details 7 The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ... Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : Zenodo, 2019 BASE Show details 8 The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ... Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : Zenodo, 2019 BASE Show details 9 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 10 Predicting insertion positions in word-level machine translation quality estimation Esplà-Gomis, Miquel; Sánchez-Martínez, Felipe; Forcada, Mikel L.. - : Elsevier, 2018 BASE Show details 11 Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful? Forcada, Mikel L.; Sánchez-Martínez, Felipe; Esplà-Gomis, Miquel. - : De Gruyter, 2017 BASE Show details 12 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 13 Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful? Forcada Mikel L.; Sánchez-Martínez Felipe; Esplà-Gomis Miquel... In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 183-195 (2017) (2017) BASE Show details 14 Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : AI Access Foundation, 2016 BASE Show details 15 RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation Pérez-Ortiz, Juan Antonio; Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe. - : De Gruyter, 2016 BASE Show details 16 Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced Pérez García, Isabel María. - 2016 BASE Show details 17 Using external sources of bilingual information for word-level quality estimation in translation technologies Esplà-Gomis, Miquel. - : Universidad de Alicante, 2016 BASE Show details 18 RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation Sánchez-Cartagena Víctor M.; Pérez-Ortiz Juan Antonio; Sánchez-Martínez Felipe In: Prague Bulletin of Mathematical Linguistics , Vol 106, Iss 1, Pp 193-204 (2016) (2016) Abstract: This paper presents ruLearn, an open-source toolkit for the automatic inference of rules for shallow-transfer machine translation from scarce parallel corpora and morphological dictionaries. ruLearn will make rule-based machine translation a very appealing alternative for under-resourced language pairs because it avoids the need for human experts to handcraft transfer rules and requires, in contrast to statistical machine translation, a small amount of parallel corpora (a few hundred parallel sentences proved to be sufficient). The inference algorithm implemented by ruLearn has been recently published by the same authors in Computer Speech & Language (volume 32). It is able to produce rules whose translation quality is similar to that obtained by using hand-crafted rules. ruLearn generates rules that are ready for their use in the Apertium platform, although they can be easily adapted to other platforms. When the rules produced by ruLearn are used together with a hybridisation strategy for integrating linguistic resources from shallow-transfer rule-based machine translation into phrase-based statistical machine translation (published by the same authors in Journal of Artificial Intelligence Research, volume 55), they help to mitigate data sparseness. This paper also shows how to use ruLearn and describes its implementation. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/98d0d649c90f44769128720f137951c9 https://doi.org/10.1515/pralin-2016-0018 BASE Hide details 19 A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora Sánchez-Cartagena, Víctor M.; Pérez-Ortiz, Juan Antonio; Sánchez-Martínez, Felipe. - : Elsevier, 2015 BASE Show details 20 Using Machine Translation to Provide Target-Language Edit Hints in Computer Aided Translation Based on Translation Memories Esplà-Gomis, Miquel; Sánchez-Martínez, Felipe; Forcada, Mikel L.. - : AI Access Foundation, 2015 BASE Show details Page: 1 2 3

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