Fusing external knowledge resources for natural language understanding techniques: A survey

Knowledge resources, e.g. knowledge graphs, which formally represent essential semantics and information for logic inference and reasoning, can compensate for the unawareness nature of many natural language processing techniques based on deep neural networks. This paper provides a focused review of the emerging but intriguing topic that fuses quality external knowledge resources in improving the performance of natural language processing tasks. Existing methods and techniques are summarised in three main categories: (1) static word embeddings, (2) sentence-level deep learning models, and (3) contextualised language representation models, depending on when, how and where external knowledge is fused into the underlying learning models. We focus on the solutions to mitigate two issues: knowledge inclusion and inconsistency between language and knowledge. Details on the design of each representative method, as well as their strength and limitation, are discussed. We also point out some potential future directions in view of the latest trends in natural language processing research.

Automated summary

This paper provides a focused review of the emerging topic of fusing external knowledge resources to improve the performance of natural language processing tasks. Three main categories of methods, based on when, how and where external knowledge is fused into learning models, are summarized. The solutions to address knowledge inclusion and inconsistency between language and knowledge are discussed, along with the design, strengths, and limitations of each representative method. Potential future research directions based on the latest trends in natural language processing are also identified.