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

Alexander Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang


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.
Anthology ID:
2020.acl-main.413
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4508–4513
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.413
DOI:
10.18653/v1/2020.acl-main.413
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.413.pdf
Video:
 http://slideslive.com/38928820