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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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Abstract:
Typically it is seen that multimodal neural machine translation (MNMT) systems trained on a combination of visual and textual inputs produce better translations than systems trained using only textual inputs. The task of such systems can be decomposed into two sub-tasks: learning visually grounded representations from images and translation of the textual counterparts using those representations. In a multi-task learning framework, translations are generated from an attention-based encoder-decoder framework and grounded representations that are learned from pretrained convolutional neural networks (CNNs) for classifying images. In this thesis, I study different computational techniques to translate the meaning of sentences from one language into another considering the visual modality as a naturally occurring meaning representation bridging between languages. We examine the behaviour of state-of-the-art MNMT systems from the data perspective in order to understand the role of the both textual and visual inputs in such systems. We evaluate our models on the Multi30k, a large-scale multilingual multimodal dataset publicly available for machine learning research. Our results in the optimal and sparse data settings show that the differences in translation system performance are proportional to the amount of both visual and linguistic information whereas, in the adversarial condition the effect of the visual modality is rather small or negligible. The chapters of the thesis follow a progression starting with using different state-of-the-art MMT models for incorporating images in optimal data settings to creating synthetic image data under the low-resource scenario and extending to addition of adversarial perturbations to the textual input for evaluating the real contribution of images.
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Keyword:
Computational linguistics; Machine learning; Machine translating
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URL: http://doras.dcu.ie/23747/
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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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