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

An example of the two-stage generation of our head-modifier template-based method.

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 automatic metrics for this task. Experiments show that our method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets.

Publication
In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Jiangjie Chen
Jiangjie Chen
Ph.D. Candidate

His research interests mainly include natural language reasoning and knowledge-guided generation.