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Lingua Custodia at WMT'19: Attempts to Control Terminology ...
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Using Monolingual Data in Neural Machine Translation: a Systematic Study
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In: Proceedings of the Third Conference on Machine Translation: Research Papers ; Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-01910235 ; Conference on Machine Translation, Oct 2018, Brussels, Belgium (2018)
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The WMT'18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English
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In: Proceedings of the Third Conference on Machine Translation ; 3rd Conference on Machine Translation (WMT 18) ; https://hal.archives-ouvertes.fr/hal-01910244 ; 3rd Conference on Machine Translation (WMT 18), Oct 2018, Bruxelles, Belgium. pp.550-564, ⟨10.18653/v1/W18-64060⟩ ; http://www.statmt.org/wmt18/ (2018)
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Word Representations in Factored Neural Machine Translation
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In: Proceedings of the Conference on Machine Translation (WMT), ; Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-01618384 ; Conference on Machine Translation, Association for Computational Linguistics, Sep 2017, Copenhagen, Denmark. pp.43 - 55 (2017)
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Learning Morphological Normalization for Translation from and into Morphologically Rich Languages
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In: ISSN: 1804-0462 ; The Prague Bulletin of Mathematical Linguistics ; https://hal.archives-ouvertes.fr/hal-01618382 ; The Prague Bulletin of Mathematical Linguistics, Univerzita Karlova v Praze, 2017, 108, pp.49-60. ⟨10.1515/pralin-2017-0008⟩ (2017)
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LIMSI@WMT'17
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In: Proceedings of the Conference on Machine Translation (WMT), ; Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-01619897 ; Conference on Machine Translation, Association for Computational Linguistics, Jan 2017, Copenhagen, Denmark. pp.257 - 264 (2017)
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Evaluating the morphological competence of Machine Translation Systems
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In: Proceedings of the Conference on Machine Translation (WMT) ; 2nd Conference on Machine Translation (WMT17) ; https://hal.archives-ouvertes.fr/hal-01618387 ; 2nd Conference on Machine Translation (WMT17), Association for Computational Linguistics, Sep 2017, Copenhague, Denmark. pp.43-55 ; http://www.statmt.org/wmt17/ (2017)
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The QT21 Combined Machine Translation System for English to Latvian
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The QT21 combined machine translation system for English to Latvian
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In: 348 ; 357 (2017)
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Learning Morphological Normalization for Translation from and into Morphologically Rich Languages
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In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 49-60 (2017) (2017)
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| 11 |
LIMSI@WMT16: Machine Translation of News
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In: First Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-01388659 ; First Conference on Machine Translation, Aug 2016, Berlin, Germany. pp.239--245, ⟨10.18653/v1/W16-2304⟩ ; https://statmt.org/wmt16 (2016)
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Unsupervised learning of morphology in the USSR
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In: Journées internationales d'Analyse statistique des Données Textuelles ; https://hal.archives-ouvertes.fr/hal-01620908 ; Journées internationales d'Analyse statistique des Données Textuelles, Damon Mayaffre, Céline Poudat, Laurent Vanni, Véronique Magri, Peter Follette, Jun 2016, Nice, France (2016)
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Abstract:
International audience ; This article deals with an important task for the processing of morphologically rich languages. Unsupervised learning of morphology mainly consists of learning a grammar that enables word segmentation into morphemes without any prior knowledge of the analysed language. It is usually assumed that the origins of such a task date back to the times of Zellig Harris, an assumption which ignores the important contribution of his contemporary, the Soviet linguist Nikolaj Dmitrievič Andreev, who developed a statistico-combinatorial model to learn morphology in the 1960s. We propose a critical description of Andreev’s model and attempt to bring to light its pioneering aspects as well as its weaknesses. Finally, we show results over several European languages. Our implementation of the model can be downloaded from https://github.com/franckbrl/stat_comb_model.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; Information Theory; Morphology; Nikolaj Andreev; Soviet Linguistics; statistico-combinatorial model; unsupervised learning; word segmentation
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URL: https://hal.archives-ouvertes.fr/hal-01620908/file/Burlot16unsupervized.pdf
https://hal.archives-ouvertes.fr/hal-01620908
https://hal.archives-ouvertes.fr/hal-01620908/document
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Two-Step MT: Predicting Target Morphology
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In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-01592337 ; International Workshop on Spoken Language Translation, 2016, Seattle, WA, United States (2016)
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Morphology-Aware Alignments for Translation to and from a Synthetic Language
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In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-01635005 ; International Workshop on Spoken Language Translation, Jan 2015, Da Nang, Vietnam (2015)
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LIMSI$@$WMT'15 : Translation Task
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In: Proceedings of the Tenth Workshop on Statistical Machine Translation ; https://hal.archives-ouvertes.fr/hal-02912383 ; Proceedings of the Tenth Workshop on Statistical Machine Translation, Sep 2015, Lisbon, Portugal. pp.145-151, ⟨10.18653/v1/W15-3016⟩ (2015)
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