What do Models Learn from Question Answering Datasets?

While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. In this paper, we investigate what models are really learning from QA datasets by evaluating BERT-based models across five popular QA datasets. We evaluate models on their generalizability to out-of-domain examples, responses to missing or incorrect information in datasets, and ability to handle variations in questions. We find that no single dataset is robust to all of our experiments and identify shortcomings in both datasets and evaluation methods. Following our analysis, we make recommendations for building future QA datasets that better evaluate the task of question answering.