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Document Sub-structure in Neural Machine Translation
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In: Proceedings of the 12th Language Resources and Evaluation Conference ; 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02900568 ; 12th Language Resources and Evaluation Conference, 2020, Marseille, France. pp.3657-3667 (2020)
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ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT'20 Metrics Shared Task
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In: Proceedings of the 5th Conference on Machine Translation ; 5th Conference on Machine Translation ; https://hal.archives-ouvertes.fr/hal-02981143 ; 5th Conference on Machine Translation, Nov 2020, Online, Unknown Region (2020)
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Detecting context-dependent sentences in parallel corpora ; Détection dans des corpus parallèles de phrases dépendantes du contexte
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In: 25th Conférence sur le Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-01800736 ; 25th Conférence sur le Traitement Automatique des Langues Naturelles, 2018, Rennes, France. pp.393-400 (2018)
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Machine Translation of Speech-Like Texts: Strategies for the Inclusion of Context
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In: 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017) ; https://hal.archives-ouvertes.fr/hal-01588152 ; 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017), Jun 2017, Orléans, France. pp.2497-2502 (2017)
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Abstract:
National audience ; Whilst the focus of Machine Translation (MT) has for a long time been the translation of planned, written texts, more and more research is being dedicated to translating speech-like texts (informal or spontaneous discourse or dialogue). To achieve high quality and natural translation of speech-like texts, the integration of context is needed, whether it is extra-linguistic (speaker identity, the interaction between speaker and interlocutor) or linguistic (coreference and stylistic phenomena linked to the spontaneous and informal nature of the texts). However, the integration of contextual information in MT systems remains limited in most current systems. In this paper, we present and critique three experiments for the integration of context into a MT system, each focusing on a different type of context and exploiting a different method: adaptation to speaker gender, cross-lingual pronoun prediction and the generation of tag questions from French into English.
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Keyword:
[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; context; contexte; gender; genre; machine translation; parole; pronoms; pronouns; speech-like texts; tag questions; traduction automatique
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URL: https://hal.archives-ouvertes.fr/hal-01588152/file/contextling.pdf https://hal.archives-ouvertes.fr/hal-01588152 https://hal.archives-ouvertes.fr/hal-01588152/document
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Cross-lingual Pronoun Prediction with Linguistically Informed Features
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In: Proceedings of the First Conference on Machine Translation ; First Conference on Machine Translation (WMT '16) ; https://hal.archives-ouvertes.fr/hal-01353862 ; First Conference on Machine Translation (WMT '16), Aug 2016, Berlin, Germany. pp.564--570, ⟨10.18653/v1/W16-2348⟩ (2016)
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