Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss

Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, Jun Zhao

Abstract
We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to be definitive. In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss. Specifically, we propose a neural encoder-decoder model with multi-level copy mechanisms to generate such questions. Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers. Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question is able to express the given predicate and correspond to a definitive answer.
Anthology ID:
D19-1247
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2431–2441
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/D19-1247/
DOI:
10.18653/v1/D19-1247
Bibkey:
Cite (ACL):
Cao Liu, Kang Liu, Shizhu He, Zaiqing Nie, and Jun Zhao. 2019. Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2431–2441, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss (Liu et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/D19-1247.pdf
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