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When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
[article]
2020
arXiv
pre-print
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
arXiv:2010.01611v2
fatcat:6hdetreda5egharx3kfo7ok7ja