Sarah Kenderdine + Merete Sanderhoff

2020
Chapitre de livre

This conversation focuses on the work of Sarah Kenderdine and Merete Sanderhoff, both of whom are based in Europe working on and within museums, as well as playing leadership roles in pan-institutional organisations such as Europeana and the Australasian Association for Digital Humanities. Topics include openness, data sovereignty, ephemeral culture, participation, empowerment and risk - and the technologies that are used to facilitate or express these concepts such as data translation, APIs, virtualisation, visualisation, simulation and immersion. This conversation was recorded on June 21, 2018. At the time, Sarah Kenderdine was Professor of Digital Museology, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland and Merete Sanderhoff was Curator of Digital Museum Practice, National Gallery of Denmark (SMK), Denmark.

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2020
Chapitre de livre

Résumé

Source officielle

https://infoscience.epfl.ch/record/311564?ln=fr

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Concepts associés (10)

Publications associées (61)

Traduction automatique

La traduction automatique désigne la traduction brute d'un texte entièrement réalisée par un ou plusieurs programmes informatiques. Dans le cas de la traduction d'une conversation audio, en direct ou en différé, on parle de transcription automatique. Un traducteur humain n’intervient pas pour corriger les erreurs du texte durant la traduction, mais seulement avant et/ou après. On la distingue de la traduction assistée par ordinateur où la traduction est en partie manuelle, éventuellement de façon interactive avec la machine.

Neural machine translation

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. They require only a fraction of the memory needed by traditional statistical machine translation (SMT) models. Furthermore, unlike conventional translation systems, all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance.

Statistical machine translation

Statistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory.

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