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Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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Multilingual neural architectures for natural language processing ; Architectures neuronales multilingues pour le traitement automatique des langues naturelles
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In: https://tel.archives-ouvertes.fr/tel-03199494 ; Informatique et langage [cs.CL]. Université du Maine, 2021. Français. ⟨NNT : 2021LEMA1002⟩ (2021)
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Multi-domain Neural Machine Translation ; Traduction automatique neuronale multidomaine
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In: https://tel.archives-ouvertes.fr/tel-03546910 ; Artificial Intelligence [cs.AI]. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG109⟩ (2021)
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A tradução e a criação de conteúdos multimodais para a comunicação de ciência
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Joint Neural Models of Word Sense Disambiguation and Machine Translation ; Modèles neuronaux joints de désambiguïsation lexicale et de traduction automatique
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In: https://tel.archives-ouvertes.fr/tel-03027723 ; Informatique et langage [cs.CL]. Université Grenoble Alpes, 2020. Français (2020)
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Joint Neural Models of Word Sense Disambiguation and Machine Translation ; Modèles neuronaux joints de désambiguïsation lexicale et de traduction automatique
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In: https://tel.archives-ouvertes.fr/tel-03033118 ; Intelligence artificielle [cs.AI]. Université Grenoble Alpes [2020-.], 2020. Français. ⟨NNT : 2020GRALM032⟩ (2020)
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Exploring deep learning techniques in the task of English-Arabic Machine Translation ; Exploration des techniques de l’apprentissage profond au service de la traduction automatique Anglais-Arabe
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In: https://tel.archives-ouvertes.fr/tel-03202829 ; Computation and Language [cs.CL]. Université des Sciences et de la Technologie Houari Boumediene (Algérie), 2020. English (2020)
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Automatic speech recognition and machine translation of Arabic and dialectal videos ; Reconnaissance et traduction automatique de la parole de vidéos arabes et dialectales
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In: https://hal.univ-lorraine.fr/tel-03132934 ; Informatique et langage [cs.CL]. Université de Lorraine, 2020. Français. ⟨NNT : 2020LORR0157⟩ (2020)
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Comment parle un robot ? ; Comment parle un robot ?: Les machines à langage dans la science-fiction
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In: https://hal.archives-ouvertes.fr/hal-02548113 ; Le Bélial', 2020, Collection Parallaxe, 978-2-84344-965-9 ; https://www.belial.fr/ (2020)
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Leveraging orthographic information to improve machine translation of under-resourced languages
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Repeat-Associated Non-Aug Translation Initiation at Expanded Ggggcc Repeats in c9orf72-Associated Amyotrophic Lateral Sclerosis and Frontotemporal Dementia
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Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation
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Abstract:
Thesis (Master's)--University of Washington, 2020 ; Neural machine translation (NMT) is a promising approach to the task of machine translation that has led to state-of-the-art results in many settings. However, NMT translations are still far from sufficient for many practical purposes. For this reason, there is a robust body of ongoing research on how to improve NMT systems with human feedback. This feedback can take many forms, including interactive-predictive NMT, post-editing of NMT output, and soliciting ratings or corrections of translations for the purposes of online learning. While these approaches are often effective, they are also often very time consuming and expensive. For that reason, there is important research into the question of how best to ensure that any human effort is used optimally. In this thesis, we contribute to this line of work by proposing a system that learns when it should ask for human feedback on a translation. This system makes use of an existing pre-trained NMT model, and introduces an additional feedback-requester model that learns to selectively solicit feedback from a human translator on the NMT translations. This system reduces human effort by directing attention to the most problematic sentences in a document, and the feedback-requester model itself is updated according to the translator's feedback. We also experiment with two active learning (AL) strategies for the feedback-requester model, and present a range of experiments simulating human translator use of the system and show the results over time.
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Keyword:
Computer science; Linguistics; natural language processing; neural machine translation
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URL: http://hdl.handle.net/1773/46824
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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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Multimodal Machine Translation ; Traduction Automatique Multimodale
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In: https://tel.archives-ouvertes.fr/tel-02309868 ; Computation and Language [cs.CL]. Le Mans Université, 2019. English. ⟨NNT : 2019LEMA1016⟩ (2019)
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Lojbanic English, An Interlingua for Parallel Machine Translation
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Formality Style Transfer Within and Across Languages with Limited Supervision
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Lost in translation, found in transliteration : books, censorship, and the evolution of the Spanish and Portuguese Jews' Congregation of London as a linguistic community, 1663-1810
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BLLDB
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UB Frankfurt Linguistik
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