When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised

Liubov Nikolenko; Pouya Rezazadeh Kalehbasti

doi:10.1007/978-3-030-84060-0_2

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arXiv · 2010.01611

When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised

Liubov Nikolenko·Pouya Rezazadeh Kalehbasti

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Abstract

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]

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Liubov Nikolenko, Pouya Rezazadeh Kalehbasti. 2020-10-04. When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised. https://doi.org/10.1007/978-3-030-84060-0_2

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