  Question Answering (QA) is key for making possible a robust communication
between human and machine. Modern language models used for QA have surpassed
the human-performance in several essential tasks; however, these models require
large amounts of human-generated training data which are costly and
time-consuming to create. This paper studies augmenting human-made datasets
with synthetic data as a way of surmounting this problem. A state-of-the-art
model based on deep transformers is used to inspect the impact of using
synthetic answerable and unanswerable questions to complement a well-known
human-made dataset. The results indicate a tangible improvement in the
performance of the language model (measured in terms of F1 and EM scores)
trained on the mixed dataset. Specifically, unanswerable question-answers prove
more effective in boosting the model: the F1 score gain from adding to the
original dataset the answerable, unanswerable, and combined question-answers
were 1.3%, 5.0%, and 6.7%, respectively. [Link to the Github repository:
https://github.com/lnikolenko/EQA]
