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- W3090656107 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. Experiment results on several knowledge-aware NLP tasks show that our proposed framework achieves superior performance by effectively leveraging knowledge in language understanding." @default.
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- W3090656107 date "2022-06-28" @default.
- W3090656107 modified "2023-10-09" @default.
- W3090656107 title "JAKET: Joint Pre-training of Knowledge Graph and Language Understanding" @default.
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- W3090656107 doi "https://doi.org/10.1609/aaai.v36i10.21417" @default.
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