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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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| 9 |
The QT21 combined machine translation system for English to Latvian
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In: 348 ; 357 (2017)
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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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| 11 |
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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| 12 |
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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Abstract:
International audience ; Most statistical translation models rely on the unsupervized computation of word-based alignments, which both serve to identify elementary translation units and to uncover hidden translation derivations. It is widely acknowledged that such alignments can only be reliably established for languages that share a sufficiently close notion of a word. When this is not the case, the usual method is to pre-process the data so as to balance the number of tokens on both sides of the corpus. In this paper, we propose a factored alignment model specifically designed to handle alignments involving a synthetic language (using the case of the Czech:English language pair). We show that this model can greatly reduce the number of non-aligned words on the English side, yielding more compact translation models, with little impact on the translation quality in our testing conditions.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; alignments; factored alignment model; machine translation; morphologically rich languages; morphology; phrase-based MT; synthetic & analytical languages
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URL: https://hal.archives-ouvertes.fr/hal-01635005
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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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