A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
Table 8: Accuracy (Acc, %) of relation ranking and winning rates (%) of pair-wise comparisons which require selecting a more reasonable sentence from two candidates, each from Correct (C), Wrong (W), or Training (T) templates.
['[BOLD] Models', '[BOLD] Acc', '[BOLD] Comparison Pairs  [BOLD] C vs. W', '[BOLD] Comparison Pairs  [ITALIC]  [BOLD] T vs. C', '[BOLD] Comparison Pairs  [ITALIC]  [BOLD] T vs. W']
(Random chance scores 33.3%). Furthermore, our model prefers the correct template compared with the wrong one (winning rate of 71.91%), and has a close preference between the training and correct templates (winning rate of 55.76%). By contrast, GPT-2 without any external knowledge relies more on frequency to score relations, and thus can hardly tell the differences among different types of sentences. The results indicate that our model can capture not only simple relevance between head and tail entities/events, but also the specific causal relations.