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Some Contributions to Interactive Machine Translation and to the Applications of Machine Translation for Historical Documents
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cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations
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In: Erofeev, Gleb, Sorokina, Irina, Han, Lifeng orcid:0000-0002-3221-2185 and Gladkoff, Serge (2021) cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations. In: Machine Translation Summit 2021, 16-20 Aug 2021, USA (online). (In Press) (2021)
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A human evaluation of English-Irish statistical and neural machine translation
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In: Dowling, Meghan orcid:0000-0003-1637-4923 , Castilho, Sheila orcid:0000-0002-8416-6555 , Moorkens, Joss orcid:0000-0003-4864-5986 , Lynn, Teresa and Way, Andy orcid:0000-0001-5736-5930 (2020) A human evaluation of English-Irish statistical and neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 6 Nov 2020, Lisbon, Portugal. (2020)
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Approches quantitatives de l'analyse des prédictions en traduction automatique neuronale (TAN)
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In: JADT 2020 : 15èmes Journées Internationales d'Analyse statistique des Données Textuelles ; https://hal.archives-ouvertes.fr/hal-03049589 ; JADT 2020 : 15èmes Journées Internationales d'Analyse statistique des Données Textuelles, Université de Toulouse, Jun 2020, Toulouse, France ; https://jadt2020.sciencesconf.org (2020)
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Machine Translation for the Normalisation of 17th c. French ; Traduction automatique pour la normalisation du français du XVII e siècle
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In: TALN 2020 ; https://hal.archives-ouvertes.fr/hal-02596669 ; TALN 2020, ATALA, Jun 2020, Nancy, France (2020)
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Optimised preprocessing for automatic mouth gesture classification ...
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UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
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In: Applied Sciences ; Volume 10 ; Issue 11 (2020)
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Entrenament d'un motor de TAE a partir d'un corpus de guies docents
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Groundbreaking technologies in machine translation ; Революционные технологии в области машинного перевода
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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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TermEval: an automatic metric for evaluating terminology translation in MT
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In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) TermEval: an automatic metric for evaluating terminology translation in MT. In: CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 07-13 Apr 2019, La Rochelle, France. (2019)
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TermEval: an automatic metric for evaluating terminology translation in MT
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In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) TermEval: an automatic metric for evaluating terminology translation in MT. In: CICLing 2019: the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 7-13 Apr 2019, La Rochelle, France. (2019)
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Building and evaluating for MT a bilingual corpus : Application ton French-Somali ; Construction et évaluation pour la TA d'un corpus journalistique bilingue : application au français-somali
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In: https://tel.archives-ouvertes.fr/tel-02269987 ; Informatique et langage [cs.CL]. Université Grenoble Alpes, 2019. Français. ⟨NNT : 2019GREAM019⟩ (2019)
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Machine Translation on a parallel Code-Switched Corpus
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In: Canadian AI 2019 - 32nd Conference on Canadian Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-02106010 ; Canadian AI 2019 - 32nd Conference on Canadian Artificial Intelligence, May 2019, Ontario, Canada (2019)
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Summarizing videos into a target language: Methodology, architectures and evaluation
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In: ISSN: 1064-1246 ; EISSN: 1875-8967 ; Journal of Intelligent and Fuzzy Systems ; https://hal.archives-ouvertes.fr/hal-02271287 ; Journal of Intelligent and Fuzzy Systems, IOS Press, 2019, 1, pp.1-12. ⟨10.3233/JIFS-179350⟩ (2019)
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A Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content
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In: The 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19) ; https://hal.archives-ouvertes.fr/hal-02270524 ; The 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19), Sep 2019, Turku, Finland ; https://nodalida2019.org/index.html (2019)
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ESSOT: an expert supporting system for ontology translation
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Abstract:
To enable knowledge access across languages, ontologies, mostly represented only in English, need to be translated into different languages. The main challenge in translating ontologies with machine translation is to disambiguate an ontology label with respect to the domain modelled by the ontology itself; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications have to implement a support system addressing this task to help experts in validating automatically generated translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit the semantic information of the label’s context to improve the quality of label translations. The system has been tested within the Organic.Lingua project by translating the modelled ontology in three languages, whereby the results are compared with translations provided by the Microsoft Translator API. The provided results demonstrate the viability of our proposed approach. ; This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 and the European Unions Horizon 2020 programme MixedEmotions (Grant Number 644632). ; non-peer-reviewed
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Keyword:
Machine Translation; Parallel Corpus; Statistical Machine Translation; Translation Service
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URL: http://hdl.handle.net/10379/14884
https://doi.org/10.1007/978-3-319-41754-7_6
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Identifying main obstacles for statistical machine translation of morphologically rich South Slavic languages
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