A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Table 6: Human evaluations of the key components.
['Models', 'Fluency', 'Coherence', 'G-Score']
The slight improvement with the skeleton extraction module in BLEU reflects as the decreases in both fluency and coherence. It suggests the necessity of human evaluation. The decreased results can be explained by the fact that the style of the dataset for pre-training the skeleton extraction module is very different from the narrative story dataset. While it may help extract some useful skeletons, it is likely that many of them are not suitable for learning the dependency of sentences. Finally, when the skeleton extraction module is trained on the target domain using reinforcement learning, the human evaluation is improved significantly by 14% on G-score.