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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)
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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
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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
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4
Fuzzy-Match Repair in Computer-Aided Translation Using Black-Box Machine Translation
Ortega, John E.
. - : Universidad de Alicante, 2021
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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
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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
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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
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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
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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)
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10
Predicting insertion positions in word-level machine translation quality estimation
Esplà-Gomis, Miquel
;
Sánchez-Martínez, Felipe
;
Forcada, Mikel L.
. - : Elsevier, 2018
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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
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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.
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Carrasco, Rafael C.
;
Pérez-Ortiz, Juan Antonio
. - : Springer Science+Business Media Dordrecht, 2017
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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)
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14
Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation
Sánchez-Cartagena, Víctor M.
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Pérez-Ortiz, Juan Antonio
;
Sánchez-Martínez, Felipe
. - : AI Access Foundation, 2016
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15
RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
Pérez-Ortiz, Juan Antonio
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Sánchez-Cartagena, Víctor M.
;
Sánchez-Martínez, Felipe
. - : De Gruyter, 2016
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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
Abstract:
Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).
Keyword:
Evaluation metrics
;
Language model
;
Lenguajes y Sistemas Informáticos
;
Phrase-based SMT
;
SMT
;
Statistical machine translation
;
Translation model
URL:
http://hdl.handle.net/10045/56346
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17
Using external sources of bilingual information for word-level quality estimation in translation technologies
Esplà-Gomis, Miquel
. - : Universidad de Alicante, 2016
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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)
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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
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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
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