  A flaw in QA evaluation is that annotations often only provide one gold
answer. Thus, model predictions semantically equivalent to the answer but
superficially different are considered incorrect. This work explores mining
alias entities from knowledge bases and using them as additional gold answers
(i.e., equivalent answers). We incorporate answers for two settings: evaluation
with additional answers and model training with equivalent answers. We analyse
three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion
increases the exact match score on all datasets for evaluation, while
incorporating it helps model training over real-world datasets. We ensure the
additional answers are valid through a human post hoc evaluation.
