Published:
2020-06-02
Proceedings:
Proceedings of the AAAI Conference on Artificial Intelligence, 34
Volume
Issue:
Vol. 34 No. 05: AAAI-20 Technical Tracks 5
Track:
AAAI Technical Track: Natural Language Processing
Downloads:
Abstract:
Neural question generation (NQG) is the task of generating questions from the given context with deep neural networks. Previous answer-aware NQG methods suffer from the problem that the generated answers are focusing on entity and most of the questions are trivial to be answered. The answer-agnostic NQG methods reduce the bias towards named entities and increasing the model's degrees of freedom, but sometimes result in generating unanswerable questions which are not valuable for the subsequent machine reading comprehension system. In this paper, we treat the answers as the hidden pivot for question generation and combine the question generation and answer selection process in a joint model. We achieve the state-of-the-art result on the SQuAD dataset according to automatic metric and human evaluation.
DOI:
10.1609/aaai.v34i05.6449
AAAI
Vol. 34 No. 05: AAAI-20 Technical Tracks 5
ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)
Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved