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In this paper we show that reporting a single performance score isinsufficient to compare non-deterministic approaches. We demonstrate for commonsequence tagging tasks that the seed value for the random number generator canresult in statistically significant (p < 10^-4) differences forstate-of-the-art systems. For two recent systems for NER, we observe anabsolute difference of one percentage point F1-score depending on the selectedseed value, making these systems perceived either as state-of-the-art ormediocre. Instead of publishing and reporting single performance scores, wepropose to compare score distributions based on multiple executions. Based onthe evaluation of 50.000 LSTM-networks for five sequence tagging tasks, wepresent network architectures that produce both superior performance as well asare more stable with respect to the remaining hyperparameters.

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging