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Annotation of prominences for the segmentation of oral corpora: the Segcor project experiment ; Annotation des proéminences pour la segmentation de corpus oraux : l'expérience du projet SegCor
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In: ISSN: 2261-2424 ; SHS Web of Conferences ; https://hal.archives-ouvertes.fr/hal-01959747 ; SHS Web of Conferences, EDP Sciences, 2018, 6e Congrès Mondial de Linguistique Française, 46 (2018)
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Automatic annotation of facial actions from a video record: The case of eyebrows raising and frowning
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In: Workshop on "Affects, Compagnons Artificiels et Interactions", WACAI 2018 ; https://hal.archives-ouvertes.fr/hal-01769684 ; Workshop on "Affects, Compagnons Artificiels et Interactions", WACAI 2018, Magalie Ochs, Jun 2018, Porquerolles, France. 7 p ; http://www.lsis.org/wacai2018/ (2018)
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Per Channel Automatic Annotation of Sign Language Motion Capture Data
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In: Workshop on the Representation and Processing of Sign Languages: Involving the Language Community, LREC 2018 ; https://hal.archives-ouvertes.fr/hal-01851404 ; Workshop on the Representation and Processing of Sign Languages: Involving the Language Community, LREC 2018, May 2018, Miyazaki Japan (2018)
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A Review Study - In Image Retrieval Bridging Of Semantic Gap ...
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A Review Study - In Image Retrieval Bridging Of Semantic Gap ...
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Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
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In: Articles (2018)
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CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings
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A Novel and Robust Approach for Pro-Drop Language Translation
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Abstract:
A significant challenge for machine translation (MT) is the phenomena of dropped pronouns (DPs), where certain classes of pronouns are frequently dropped in the source language but should be retained in the target language. In response to this common problem, we propose a semi-supervised approach with a universal framework to recall missing pronouns in translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation has two phases: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our statistical MT (SMT) system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. To validate the robustness of our approach, we investigate our approach on both Chinese–English and Japanese–English corpora extracted from movie subtitles. Compared with an SMT baseline system, experimental results show that our approach achieves a significant improvement of++1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy for Chinese–English, and nearly++1 BLEU point with 58% F-score for Japanese–English. We believe that this work could help both MT researchers and industries to boost the performance of MT systems between pro-drop and non-pro-drop languages.
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Keyword:
Dropped pronoun annotation; Dropped pronoun generation; Machine translation; Multilayer perceptron; Pro-drop language; Recurrent neural networks; Semi-supervised approach
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URL: https://doi.org/10.1007/s10590-016-9184-9 http://dspace.stir.ac.uk/bitstream/1893/24678/1/Wang_etal_MachTranslat_2017.pdf http://hdl.handle.net/1893/24678
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Eesti lastekeele korpuse morfoloogilise märgendamise kitsaskohtadest
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In: Eesti Rakenduslingvistika Ühingu Aastaraamat, Vol 13, Pp 205-221 (2017) (2017)
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Expériences sur l’analyse morphosyntaxique des corpus oraux avec l’annotateur multi-niveaux DisMo
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In: Corela (2017) (2017)
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