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

• URL: http://arxiv.org/abs/2010.01611v2
• Date: Mon, 19 Oct 2020 02:29:13 GMT
• Title: When in Doubt, Ask: Generating Answerable and Unanswerable Questions, Unsupervised
• Authors: Liubov Nikolenko, Pouya Rezazadeh Kalehbasti
• Abstract summary: 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. This paper studies augmenting human-made datasets with synthetic data as a way of surmounting this problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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