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Automatic Extraction of Verb Paradigms in Regional Languages: the case of the Linguistic Crescent varieties
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In: STLU (Spoken Language Technologies for Under-resourced languages) ; https://halshs.archives-ouvertes.fr/halshs-02508210 ; STLU (Spoken Language Technologies for Under-resourced languages), European Language Resources Association (ELRA), Jan 2020, Marseille, France. pp.245-249 ; https://lrec2020.lrec-conf.org/en/ (2020)
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Automatic Extraction of Verb Paradigms in Regional Languages: the case of the Linguistic Crescent varieties
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In: STLU (Spoken Language Technologies for Under-resourced languages) ; https://halshs.archives-ouvertes.fr/halshs-02508210 ; STLU (Spoken Language Technologies for Under-resourced languages), European Language Resources Association (ELRA), Jan 2020, Marseille, France. pp.245-249 ; https://lrec2020.lrec-conf.org/en/ (2020)
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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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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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Abstract:
International audience ; This paper describes a two-step machine translation system that addresses the issue of translating into a morphologically rich language (English to Czech), by performing separately the translation and the generation of target morphology. The first step consists in translating from English into a normalized version of Czech, where some morphological information has been removed. The second step retrieves this information and re-inflects the normalized output, turning it into fully inflected Czech. We introduce different setups for the second step and evaluate the quality of their predictions over different MT systems trained on different amounts of parallel and monolingual data and report ways to adapt to different data sizes, which improves the translation in low-resource conditions, as well as when large training data is available.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.archives-ouvertes.fr/hal-01592337/document https://hal.archives-ouvertes.fr/hal-01592337/file/Burlot16reinflection.pdf https://hal.archives-ouvertes.fr/hal-01592337
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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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Apprentissage partiellement supervisé d'un étiqueteur morpho-syntaxique par transfert cross-lingue
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In: Conférence sur le Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-01908360 ; Conférence sur le Traitement Automatique des Langues Naturelles, Jan 2014, Marseille, France (2014)
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