Evaluation Our evaluation methodology follows <cite>Levy et al. (2017)</cite> . We compute precision, recall and F1 by comparing spans predicted by the 3 https://github.com/google-research/ bert/blob/master/multilingual.md models with gold answers. Precision is equal to the true positives divided by total number of nonnil answers predicted by a system. Recall is equal to the true positives divided by the total number of instances that are non-nil in the ground truth answers. Word order and punctuation are not considered.