  We present the results of the Machine Reading for Question Answering (MRQA)
2019 shared task on evaluating the generalization capabilities of reading
comprehension systems. In this task, we adapted and unified 18 distinct
question answering datasets into the same format. Among them, six datasets were
made available for training, six datasets were made available for development,
and the final six were hidden for final evaluation. Ten teams submitted
systems, which explored various ideas including data sampling, multi-task
learning, adversarial training and ensembling. The best system achieved an
average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points
higher than our initial baseline based on BERT.
