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An Introduction to Complex Systems: Making Sense of a Changing World
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In: Faculty Books (2019)
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Should we use movie subtitles to study linguistic patterns of conversational speech? A study based on French, English and Taiwan Mandarin
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In: Third International Symposium on Linguitic Patters of Spontaneous Speech ; https://hal.archives-ouvertes.fr/hal-02385689 ; Third International Symposium on Linguitic Patters of Spontaneous Speech, Nov 2019, Taipei, Taiwan ; http://lpss2019.ling.sinica.edu.tw/ (2019)
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Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019)
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A computational account of virtual travelers in the Montagovian generative lexicon
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In: The Semantics of Dynamic Space in French ; https://hal.archives-ouvertes.fr/hal-02093536 ; Michel Aurnague; Dejan Stosic. The Semantics of Dynamic Space in French, John Benjamins, pp.407-450, 2019, Part IV. Formal and computational aspects of motion-based narrations, 9789027203205. ⟨10.1075/hcp.66.09lef⟩ ; https://benjamins.com/catalog/hcp.66.09lef (2019)
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Towards TreeLex++: Syntactico-Semantic Lexical Resource for French
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In: Language & Technology Conference ; https://hal.archives-ouvertes.fr/hal-02120183 ; Language & Technology Conference, May 2019, Poznan, Poland (2019)
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On the integration of linguistic features into statistical and neural machine translation
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In: Vanmassenhove, Eva Odette Jef orcid:0000-0003-1162-820X (2019) On the integration of linguistic features into statistical and neural machine translation. PhD thesis, Dublin City University. (2019)
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Neural machine translation for multimodal interaction
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Dutta Chowdhury, Koel. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019
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In: Dutta Chowdhury, Koel (2019) Neural machine translation for multimodal interaction. Master of Science thesis, Dublin City University. (2019)
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Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study
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In: Barry, James orcid:0000-0003-3051-585X , Wagner, Joachim orcid:0000-0002-8290-3849 and Foster, Jennifer orcid:0000-0002-7789-4853 (2019) Cross-lingual parsing with polyglot training and multi-treebank learning: a Faroese case study. In: The 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), 3 - 5 Nov 2019, Hong Kong, China. ISBN 978-1-950737-78-9 (2019)
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Selecting artificially-generated sentences for fine-tuning neural machine translation
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 and Way, Andy orcid:0000-0001-5736-5930 (2019) Selecting artificially-generated sentences for fine-tuning neural machine translation. In: 12th International Conference on Natural Language Generation, 29 Oct - 1 Nov 2019, Tokyo, Japan. (2019)
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Automatic processing of code-mixed social media content
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Barman, Utsab. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019
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In: Barman, Utsab (2019) Automatic processing of code-mixed social media content. PhD thesis, Dublin City University. (2019)
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Automatic error classification with multiple error labels
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In: Popović, Maja orcid:0000-0001-8234-8745 and Vilar, David (2019) Automatic error classification with multiple error labels. In: MT Summit XVII, 19 - 23 Aug 2019, Dublin, Ireland. (2019)
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Improving transductive data selection algorithms for machine translation
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Poncelas, Alberto. - : Dublin City University. School of Computing, 2019. : Dublin City University. ADAPT, 2019
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 (2019) Improving transductive data selection algorithms for machine translation. PhD thesis, Dublin City University. (2019)
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Combining SMT and NMT back-translated data for efficient NMT
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In: Poncelas, Alberto orcid:0000-0002-5089-1687 , Popović, Maja orcid:0000-0001-8234-8745 , Shterionov, Dimitar orcid:0000-0001-6300-797X , Maillette de Buy Wenniger, Gideon and Way, Andy orcid:0000-0001-5736-5930 (2019) Combining SMT and NMT back-translated data for efficient NMT. In: Recent Advances in Natural Language Processing (RANLP 2019), 2-4 Sept 2019, Varna, Bulgaria. (2019)
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Abstract:
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation (Sennrich et al., 2016), which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model. Generally, NMT models are used for back-translation. In this work, we analyze the performance of models when the training data is extended with synthetic data using different MT approaches. In particular we investigate back-translated data generated not only by NMT but also by Statistical Machine Translation (SMT) models and combinations of both. The results reveal that the models achieve the best performances when the training set is augmented with back-translated data created by merging different MT approaches.
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Keyword:
Computational linguistics; Machine translating
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URL: http://doras.dcu.ie/24272/
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