@inproceedings{liu-etal-2019-generating,
title = "Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss",
author = "Liu, Cao and
Liu, Kang and
He, Shizhu and
Nie, Zaiqing and
Zhao, Jun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1247",
doi = "10.18653/v1/D19-1247",
pages = "2431--2441",
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.",
}
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%0 Conference Proceedings
%T Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss
%A Liu, Cao
%A Liu, Kang
%A He, Shizhu
%A Nie, Zaiqing
%A Zhao, Jun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F liu-etal-2019-generating
%X 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.
%R 10.18653/v1/D19-1247
%U https://aclanthology.org/D19-1247
%U https://doi.org/10.18653/v1/D19-1247
%P 2431-2441
Markdown (Informal)
[Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss](https://aclanthology.org/D19-1247) (Liu et al., EMNLP-IJCNLP 2019)
ACL