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Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output ...
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Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ...
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Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ...
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Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ...
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A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment ...
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Automatic classification of human translation and machine translation : a study from the perspective of lexical diversity
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Transformer-based NMT : modeling, training and implementation
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Xu, Hongfei. - : Saarländische Universitäts- und Landesbibliothek, 2021
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
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In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020)
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ...
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Linguistically inspired morphological inflection with a sequence to sequence model ...
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Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ...
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Language service provision in the 21st century: challenges, opportunities and educational perspectives for translation studies
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In: ISBN: 9788869234934 ; Bologna Process beyond 2020: Fundamental values of the EHEA pp. 297-303 (2020)
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Abstract:
Translators have contributed significantly to the evolution of culture and to ever-increasing globalization. With advances in AI, notably in Machine Translation, new opportunities and challenges have emerged. Increased recognition of language as a human right and not-for-profit translation have added to opportunities and challenges within the global translation sector. This in turn creates opportunities and challenges for training of translators in the higher education sector. Translation Studies as an academic discipline has sought to agree on competence models that guide teaching practice. However, with the speed of change in AI especially, the discipline needs to assess how competence requirements will change and what the translator of the future will need to do. We propose to expand the types of skills currently taught and to do this through collaborative programs across EU universities.
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Keyword:
Artificial Intelligence; Higher Education; info:eu-repo/classification/ddc/410.2; Language Service Provision; Machine Translation; Translation Competence
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URL: https://archive-ouverte.unige.ch/unige:138544
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Deep interactive text prediction and quality estimation in translation interfaces
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In: Hokamp, Christopher M. (2018) Deep interactive text prediction and quality estimation in translation interfaces. PhD thesis, Dublin City University. (2018)
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