Learning to Predict Explainable Plots for Neural Story Generation
Table 2: Human evaluation on the ROCStories and VIST datasets. We use “F” to denote fluency, “R” to denote relevance, “C” to denote coherence, and “M” to denote the geometric mean of fluency, relevance, and coherence.
['Model', 'ROCStories F ↑', 'ROCStories R ↑', 'ROCStories C ↑', 'ROCStories M ↑', 'VIST F ↑', 'VIST R ↑', 'VIST C ↑', 'VIST M ↑']
While all the six methods achieve close fluency scores because of the use of neural networks, the gaps on relevance and coherence are much larger. On the ROCStories dataset, our approach achieves significantly better results than all baselines in terms of fluency, relevance, and G-Mean except that Separate obtains a slightly higher coherence score than our approach. On the VIST dataset, our method achieves the highest scores on all the metrics by large margins. These differences are statistically significant (p<0.01).