  We compare three new datasets for question answering: SQuAD 2.0, QuAC, and
CoQA, along several of their new features: (1) unanswerable questions, (2)
multi-turn interactions, and (3) abstractive answers. We show that the datasets
provide complementary coverage of the first two aspects, but weak coverage of
the third. Because of the datasets' structural similarity, a single extractive
model can be easily adapted to any of the datasets and we show improved
baseline results on both SQuAD 2.0 and CoQA. Despite the similarity, models
trained on one dataset are ineffective on another dataset, but we find moderate
performance improvement through pretraining. To encourage cross-evaluation, we
release code for conversion between datasets at
https://github.com/my89/co-squac .
