Ensuring Readability and Data-fidelity using Head-modifier Templates
in Deep Type Description Generation
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 stateof-the-art performance on both datasets.