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Knowledge-enhanced language representation learning has shown promisingresults across various knowledge-intensive NLP tasks. However, prior methodsare limited in efficient utilization of multilingual knowledge graph (KG) datafor language model (LM) pretraining. They often train LMs with KGs in indirectways, relying on extra entity/relation embeddings to facilitate knowledgeinjection. In this work, we explore methods to make better use of themultilingual annotation and language agnostic property of KG triples, andpresent novel knowledge based multilingual language models (KMLMs) traineddirectly on the knowledge triples. We first generate a large amount ofmultilingual synthetic sentences using the Wikidata KG triples. Then based onthe intra- and inter-sentence structures of the generated data, we designpretraining tasks to enable the LMs to not only memorize the factual knowledgebut also learn useful logical patterns. Our pretrained KMLMs demonstratesignificant performance improvements on a wide range of knowledge-intensivecross-lingual tasks, including named entity recognition (NER), factualknowledge retrieval, relation classification, and a newly designed logicalreasoning task.

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples