Publication: A Set of Recommendations for Assessing Human–Machine Parity in Language Translation
A Set of Recommendations for Assessing Human–Machine Parity in Language Translation
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Läubli, S., Castilho, S., Neubig, G., Sennrich, R., Shen, Q., & Toral, A. (2020). A Set of Recommendations for Assessing Human–Machine Parity in Language Translation. Journal of Artificial Intelligence Research, 67, 653–672. https://doi.org/10.1613/jair.1.11371
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Abstract
The quality of machine translation has increased remarkably over the past years, to the degree that it was found to be indistinguishable from professional human translation in a number of empirical investigations. We reassess Hassan et al.'s 2018 investigation into Chinese to English news translation, showing that the finding of human–machine parity was owed to weaknesses in the evaluation design—which is currently considered best practice in the field. We show that the professional human translations contained significantly fewer err
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Läubli, S., Castilho, S., Neubig, G., Sennrich, R., Shen, Q., & Toral, A. (2020). A Set of Recommendations for Assessing Human–Machine Parity in Language Translation. Journal of Artificial Intelligence Research, 67, 653–672. https://doi.org/10.1613/jair.1.11371
