  If a question cannot be answered with the available information, robust
systems for question answering (QA) should know _not_ to answer. One way to
build QA models that do this is with additional training data comprised of
unanswerable questions, created either by employing annotators or through
automated methods for unanswerable question generation. To show that the model
complexity of existing automated approaches is not justified, we examine a
simpler data augmentation method for unanswerable question generation in
English: performing antonym and entity swaps on answerable questions. Compared
to the prior state-of-the-art, data generated with our training-free and
lightweight strategy results in better models (+1.6 F1 points on SQuAD 2.0 data
with BERT-large), and has higher human-judged relatedness and readability. We
quantify the raw benefits of our approach compared to no augmentation across
multiple encoder models, using different amounts of generated data, and also on
TydiQA-MinSpan data (+9.3 F1 points with BERT-large). Our results establish
swaps as a simple but strong baseline for future work.
