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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; Sánchez Cartagena, Víctor Manuel; Sánchez-Martínez, Felipe. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.669/ 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 ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Machine translation; Natural Language Processing URL: https://underline.io/lecture/37747-rethinking-data-augmentation-for-low-resource-neural-machine-translation-a-multi-task-learning-approach https://dx.doi.org/10.48448/wkq6-2f54 BASE Hide 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) BASE Show 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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