Humans Outperform Machines at the Bilingual Shannon Game
AbstractWe 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
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Ghazvininejad, M.; Knight, K. Humans Outperform Machines at the Bilingual Shannon Game. Entropy 2017, 19, 15.
Ghazvininejad M, Knight K. Humans Outperform Machines at the Bilingual Shannon Game. Entropy. 2017; 19(1):15.Chicago/Turabian Style
Ghazvininejad, Marjan; Knight, Kevin. 2017. "Humans Outperform Machines at the Bilingual Shannon Game." Entropy 19, no. 1: 15.
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