K-BERT: Enabling Language Representation with Knowledge Graph
Table 2: Results of various models on NLPCC-DBQA (MRR %) and MSRA-NER (F1 %).
['[BOLD] Models\\Datasets', '[BOLD] NLPCC-DBQA  [ITALIC] Dev', '[BOLD] NLPCC-DBQA  [ITALIC] Test', '[BOLD] MSRA-NER  [ITALIC] Dev', '[BOLD] MSRA-NER  [ITALIC] Test']
Each of the above datasets is divided into three parts: train, dev, and test. We use the train part to fine-tune the model and then evaluate its performance on the dev and test parts. The language KG (HowNet) performs better than the encyclopedic KG in terms of semantic similarity tasks (i.e., XNLI and LCQMC); (3) For Q&A and NER tasks (i.e., NLPCC-DBQA and MSRA-NER), the encyclopedic KG (CN-DBpedia) is more suitable than the language KG. Therefore, it is important to choose the right KG based on the type of task. In addition, it can be observed that the use of an additional corpus (WebtextZh) can also bring performance boost, but not as significant as KG.