  Question answering (QA) models for reading comprehension have been
demonstrated to exploit unintended dataset biases such as question-context
lexical overlap. This hinders QA models from generalizing to under-represented
samples such as questions with low lexical overlap. Question generation (QG), a
method for augmenting QA datasets, can be a solution for such performance
degradation if QG can properly debias QA datasets. However, we discover that
recent neural QG models are biased towards generating questions with high
lexical overlap, which can amplify the dataset bias. Moreover, our analysis
reveals that data augmentation with these QG models frequently impairs the
performance on questions with low lexical overlap, while improving that on
questions with high lexical overlap. To address this problem, we use a synonym
replacement-based approach to augment questions with low lexical overlap. We
demonstrate that the proposed data augmentation approach is simple yet
effective to mitigate the degradation problem with only 70k synthetic examples.
Our data is publicly available at
https://github.com/KazutoshiShinoda/Synonym-Replacement.
