Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering

• URL: http://arxiv.org/abs/2004.11892v1
• Date: Fri, 24 Apr 2020 17:57:45 GMT
• Title: Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering
• Authors: Alexander R. Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
• Abstract summary: We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
• Score: 98.48363619128108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Question Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has been to fine-tune a pretrained language model on a task-specific labeled dataset. This paradigm, however, relies on scarce, and costly to obtain, large-scale human-labeled data. We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance by allowing the model to learn more complex context-question relationships. Training a QA model on this data gives a relative improvement over a previous unsupervised model in F1 score on the SQuAD dataset by about 14%, and 20% when the answer is a named entity, achieving state-of-the-art performance on SQuAD for unsupervised QA.

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