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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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Abstract:
Recent years have seen an increased interest in machine translation technologies and applications due to an increasing need to overcome language barriers in many sectors. New machine translations technologies are emerging rapidly and with them, bold claims of achieving human parity such as: (i) the results produced approach "accuracy achieved by average bilingual human translators [on some test sets]" (Wu et al., 2017b) or (ii) the "translation quality is at human parity when compared to professional human translators" (Hassan et al., 2018) have seen the light of day (Läubli et al., 2018). Aside from the fact that many of these papers craft their own definition of human parity, these sensational claims are often not supported by a complete analysis of all aspects involved in translation. Establishing the discrepancies between the strengths of statistical approaches to machine translation and the way humans translate has been the starting point of our research. By looking at machine translation output and linguistic theory, we were able to identify some remaining issues. The problems range from simple number and gender agreement errors to more complex phenomena such as the correct translation of aspectual values and tenses. Our experiments confirm, along with other studies (Bentivogli et al., 2016), that neural machine translation has surpassed statistical machine translation in many aspects. However, some problems remain and others have emerged. We cover a series of problems related to the integration of specific linguistic features into statistical and neural machine translation, aiming to analyse and provide a solution to some of them. Our work focuses on addressing three main research questions that revolve around the complex relationship between linguistics and machine translation in general. By taking linguistic theory as a starting point we examine to what extent theory is reflected in the current systems. We identify linguistic information that is lacking in order for automatic translation systems to produce more accurate translations and integrate additional features into the existing pipelines. We identify overgeneralization or 'algorithmic bias' as a potential drawback of neural machine translation and link it to many of the remaining linguistic issues.
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Keyword:
Algorithmic Bias; Artificial intelligence; Aspect; Computational linguistics; French language; Gender; Gender Agreement; Gender Bias; Language; Lexical Diversity; Lexical Loss; Linguistic Loss; Linguistics; Machine learning; Machine translating; Neural Machine Translation; Spanish language; Statistical Machine Translation; Subject-verb Agreement; Tense; Translating and interpreting
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URL: http://doras.dcu.ie/23714/
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| 14 |
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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| 15 |
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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