What do Models Learn from Question Answering Datasets?
This work addresses the problem of dataset quality and evaluation for reading comprehension in QA, which is incremental as it builds on existing QA research to improve future datasets.
The paper investigates whether models learn reading comprehension from QA datasets by evaluating BERT-based models across five datasets, finding that no dataset is robust to all experiments and identifying shortcomings in datasets and evaluation methods.
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 if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets. We evaluate models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. 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 through reading comprehension. We also release code to convert QA datasets to a shared format for easier experimentation at https://github.com/amazon-research/qa-dataset-converter.