K-BERT: Enabling Language Representation with Knowledge Graph
Table 3: Results of various models on specific-domain tasks (%).
['[BOLD] Models\\Datasets', '[BOLD] Finance_Q&A  [ITALIC] P.', '[BOLD] Finance_Q&A  [ITALIC] R.', '[BOLD] Finance_Q&A  [ITALIC] F1', '[BOLD] Law_Q&A  [ITALIC] P.', '[BOLD] Law_Q&A  [ITALIC] R.', '[BOLD] Law_Q&A  [ITALIC] F1', '[BOLD] Finance_NER  [ITALIC] P.', '[BOLD] Finance_NER  [ITALIC] R.', '[BOLD] Finance_NER  [ITALIC] F1', '[BOLD] Medicine_NER  [ITALIC] P.', '[BOLD] Medicine_NER  [ITALIC] R.', '[BOLD] Medicine_NER  [ITALIC] F1']
Similarly, the specific-domain datasets are split into three parts: train, dev, and test, which are used to fine-tune, select and test model, respectively. Compared with BERT, K-BERT has a significant performance improvement in terms of domain tasks. As for F1, K-BERT with CN-DBpedia can improve the performance of all tasks by 1∼2%. The unique gain benefits from the domain knowledge in KG. From these results, we can conclude that KG, especially the domain KG, is very helpful for domain-specific tasks.