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Entropy 2017, 19(1), 15;

Humans Outperform Machines at the Bilingual Shannon Game

Information Sciences Institute, University of Southern California, 4676 Admiralty Way #1001, Marina Del Rey, CA 90292, USA
Both authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editors: Kevin H. Knuth and Raúl Alcaraz Martínez
Received: 3 October 2016 / Revised: 8 December 2016 / Accepted: 27 December 2016 / Published: 30 December 2016
(This article belongs to the Section Information Theory, Probability and Statistics)
Full-Text   |   PDF [367 KB, uploaded 30 December 2016]   |  


We provide an upper bound for the amount of information a human translator adds to an original text, i.e., how many bits of information we need to store a translation, given the original. We do this by creating a Bilingual Shannon Game that elicits character guesses from human subjects, then developing models to estimate the entropy of those guess sequences. View Full-Text
Keywords: compression; multilingual; translation compression; multilingual; translation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ghazvininejad, M.; Knight, K. Humans Outperform Machines at the Bilingual Shannon Game. Entropy 2017, 19, 15.

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