Commonsense Evidence Generation and Injection in Reading Comprehension
Table 3: Generation performance on ConceptNet
['Model', 'PPL', 'Score', 'N/T sro', 'N/T o']
Even though the generated tuples on LSTM-s and CKGB model has high “N/T sro” (both are 86.25%) and “N/T o” (7.83% and 8.67% respectively), which means they generate novel relations and expand the knowledge graph, the generated nodes and relations may not be correct. We still need to rely on the Score to evaluate and they do poorly (60.83% and 57.17% respectively) in terms of Score. Since our proposed CEGI-GPT2 and CEGI-BERT model have high Score and low PPL, we believe that both models can produce high-quality knowledge and still be able to extend the size of the knowledge graph.