Emerging technologies: Innovations and disruptions

Hélène Pielmeier
MultiLingual Jul/Aug 2016
Core Focus

Deep learning technologies will eventually deconstruct linguistic and semantic components, leveraging that computing power and neural network to improve the quality and usability of the translated content. For example, Microsoft recently released neural net-based translation apps with offline capability for both iOS and Android. While their quality is poor, the fact that they can run at all on limited hardware is an impressive achievement....

Translation or interpreting output, regardless of its human or machine provenance, can only be so good, so both end-buyers and LSPs are aware of the need to improve source context. Automatic content enrichment (ACE) is an emerging area of interest. Solutions such as Acrolinx and Open Calais are achieving increased traction in the space. ACE applies natural language processing to add links and metadata to the source or target text to make information more accessible and useful. ACE, combined with human curation, allows localization experts to add relevant information and links not found in the source to particular target language versions. Localizers have just started to explore the potential. The European Union’s FREME project is developing an open framework to support multilingual ACE services alongside services such as machine translation and content internationalization....

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