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This paper argues for the widest possible use of bootstrap confidenceintervals for comparing NLP system performances instead of the state-of-the-artstatus (SOTA) and statistical significance testing. Their main benefits are todraw attention to the difference in performance between two systems and to helpassessing the degree of superiority of one system over another. Two casesstudies, one comparing several systems and the other based on a K-foldcross-validation procedure, illustrate these benefits. A python module forobtaining these confidence intervals as well as a second function implementingthe Fisher-Pitman test for paired samples are freely available on PyPi.

Please, Don't Forget the Difference and the Confidence Interval when Seeking for the State-of-the-Art Status