Learning to Predict Explainable Plots for Neural Story Generation
Table 1: Automatic evaluation on the ROCStories and VIST datasets. We use “B-4”, “MET”, “R-L”, and “PPL” to denote BLEU-4, METEOR, ROUGE-L, and perplexity scores, respectively.
['Model', 'ROCStories B-4 ↑', 'ROCStories MET ↑', 'ROCStories R-L ↑', 'ROCStories PPL ↓', 'VIST B-4 ↑', 'VIST MET ↑', 'VIST R-L ↑', 'VIST PPL ↓']
Skeleton generally performs worse than other baselines except that it obtains lower perplexities than Separate on both datasets. Surprisingly, although Direct does not use any high-level plot, it outperforms other baselines in terms of BLEU, METEOR, and perplexity. One possible reason is that the stories in both datasets are relatively short. The superiority of hierarchical models over direct mapping might increase when generating very long stories.