A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
Table 7: Accuracy of beginning ranking and logic ranking. Larger scores are better.
['[BOLD] Models', '[BOLD] BR\xa0(%)', '[BOLD] LR\xa0(%)']
For BR, we first sampled 9 negative beginnings (first sentence) for a true story, and then calculated the perplexity of the 10 stories. If the true story has the lowest perplexity by our model, it is regarded as a correct prediction. As for LR, since each story in ROCStories consists of five sentences, we produced four shuffled versions by switching each pair of adjacent sentences. We then used our model to score the five stories with perplexity. A prediction is regarded as correct if the true story has the lowest score. We randomly sampled 1,000 human-written stories from the test set in our evaluation.