Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Zhou Yu, Chengqing Zong
Abstract
Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized. ii) Granularity mismatch: the current KG methods utilize the entity as the basic granularity, while NMT utilizes the sub-word as the granularity, making the KG different to be utilized in NMT.
Topics & Concepts
GranularityComputer scienceMachine translationSentenceNatural language processingTask (project management)Artificial intelligenceGraphKnowledge graphTranslation (biology)Machine learningTheoretical computer scienceProgramming languageBiochemistryEconomicsGeneManagementChemistryMessenger RNANatural Language Processing TechniquesTopic ModelingText Readability and Simplification