Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation

Jiangjie Chen, Ao Wang, Haiyun Jiang, Suo Feng, Chenguang Li, Yanghua Xiao


Abstract
A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative methods either overlook the grammatical structure or make factual mistakes in generated texts. To solve these problems, we propose a head-modifier template based method to ensure the readability and data fidelity of generated type descriptions. We also propose a new dataset and two metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.
Anthology ID:
P19-1196
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2036–2046
Language:
URL:
https://aclanthology.org/P19-1196
DOI:
10.18653/v1/P19-1196
Bibkey:
Cite (ACL):
Jiangjie Chen, Ao Wang, Haiyun Jiang, Suo Feng, Chenguang Li, and Yanghua Xiao. 2019. Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2036–2046, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation (Chen et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1196.pdf
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