JAKET: Joint Pre-training of Knowledge Graph and Language Understanding

• URL: http://arxiv.org/abs/2010.00796v1
• Date: Fri, 2 Oct 2020 05:53:36 GMT
• Title: JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
• Authors: Donghan Yu, Chenguang Zhu, Yiming Yang, Michael Zeng
• Abstract summary: We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains.
• Score: 73.43768772121985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graphs (KGs) contain rich information about world knowledge, entities and relations. Thus, they can be great supplements to existing pre-trained language models. However, it remains a challenge to efficiently integrate information from KG into language modeling. And the understanding of a knowledge graph requires related context. We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language. The knowledge module and language module provide essential information to mutually assist each other: the knowledge module produces embeddings for entities in text while the language module generates context-aware initial embeddings for entities and relations in the graph. Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains. Experimental results on several knowledge-aware NLP tasks show that our proposed framework achieves superior performance by effectively leveraging knowledge in language understanding.

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