Neural Network-Based Tree Translation for Knowledge Base Construction

Abstract

Knowledge bases (KB), such as Probase and ConceptNet, play an important role in many natural language processing tasks. Compared with resource-poor languages such as Chinese, the scale and quality of English knowledge bases are obviously superior. To expand Chinese KBs by using English KB resources, translating English KBs into Chinese is an effective way. In this direction, two major challenges are how to model more structure semantics to improve translation quality and how to avoid labor-intensive feature engineering. We address these challenges by presenting a neural network approach, which learns tree representation by different structure features. We also build a new dataset for English-Chinese KB translation from Probase and ConceptNet, and compare our proposed approach with several baselines on it. Experimental results show that the proposed method improves the translation accuracy compared with baseline methods. Meanwhile, we translate Probase and ConceptNet into Zh-Probase and Zh-ConceptNet by our proposed model, and release them to the public, in hope of speeding up the research in Chinese natural language processing tasks.

Keywords

• Knowledge base construction
• machine translation
• named entity disambiguation
• natural language processing
• representation learning
• word sense disambiguation