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MultiTraiNMT: training materials to approach neural machine translation from scratch ; MultiTraiNMT: des outils pour se former à la traduction automatique neuronale
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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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach ...
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
Abstract: In the context of neural machine translation, data augmentation (DA) techniques may be used for generating additional training samples when the available parallel data are scarce. Many DA approaches aim at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words, thus making it closer to the true data distribution of parallel sentences. In this paper, we propose to follow a completely different approach and present a multi-task DA approach in which we generate new sentence pairs with transformations, such as reversing the order of the target sentence, which produce unfluent target sentences. During training, these augmented sentences are used as auxiliary tasks in a multi-task framework with the aim of providing new contexts where the target prefix is not informative enough to predict the next word. This strengthens the encoder and forces the decoder to pay more attention to the source representations of the encoder. Experiments carried out on six low-resource translation tasks show consistent improvements over the baseline and over DA methods aiming at extending the support of the empirical data distribution. The systems trained with our approach rely more on the source tokens, are more robust against domain shift and suffer less hallucinations. ; Work funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement number 825299, project Global Under-Resourced Media Translation (GoURMET); and by Generalitat Valenciana through project GV/2021/064. The computational resources used for the experiments were funded by the European Regional Development Fund through project IDIFEDER/2020/003.
Keyword: Data augmentation; Lenguajes y Sistemas Informáticos; Multi-task learning approach; Neural machine translation
URL: https://doi.org/10.18653/v1/2021.emnlp-main.669
http://hdl.handle.net/10045/121939
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Fuzzy-Match Repair in Computer-Aided Translation Using Black-Box Machine Translation
Ortega, John E.. - : Universidad de Alicante, 2021
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Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation ...
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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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The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ...
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The Universitat d'Alacant submissions to the English-to-Kazakh news translation task at WMT 2019 ...
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Reading comprehension of machine translation output: what makes for a better read?
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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Predicting insertion positions in word-level machine translation quality estimation
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Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful?
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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
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Towards Optimizing MT for Post-Editing Effort: Can BLEU Still Be Useful?
In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 183-195 (2017) (2017)
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Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation
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RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
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Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced
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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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RuLearn: an Open-source Toolkit for the Automatic Inference of Shallow-transfer Rules for Machine Translation
In: Prague Bulletin of Mathematical Linguistics , Vol 106, Iss 1, Pp 193-204 (2016) (2016)
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A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora
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Using Machine Translation to Provide Target-Language Edit Hints in Computer Aided Translation Based on Translation Memories
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