  In this paper we show that reporting a single performance score is
insufficient to compare non-deterministic approaches. We demonstrate for common
sequence tagging tasks that the seed value for the random number generator can
result in statistically significant (p < 10^-4) differences for
state-of-the-art systems. For two recent systems for NER, we observe an
absolute difference of one percentage point F1-score depending on the selected
seed value, making these systems perceived either as state-of-the-art or
mediocre. Instead of publishing and reporting single performance scores, we
propose to compare score distributions based on multiple executions. Based on
the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we
present network architectures that produce both superior performance as well as
are more stable with respect to the remaining hyperparameters.
