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From feature to paradigm: deep learning in machine translation

Author
Ruiz, M.
Type of activity
Journal article
Journal
Journal of artificial intelligence research
Date of publication
2018-04-01
Volume
61
First page
947
Last page
974
Repository
http://hdl.handle.net/2117/116854 Open in new window
URL
https://jair.org/index.php/jair/article/view/11198 Open in new window
Abstract
In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feed- forward neural networks, recurrent neural networks and the encoder-d...
Citation
Ruiz, M. From feature to paradigm: deep learning in machine translation. "Journal of artificial intelligence research", 1 Abril 2018, vol. 61, p. 947-974.
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants

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