A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
Table 10: Distribution of error types for different models.
['[BOLD] Error Type', '[BOLD] Ours', '[BOLD] Fusion', '[BOLD] GPT-2\xa0(Fine-tune)']
We manually annotated four types of error from the lost stories: repetition (repeating the same scenes), unrelated entities or events (with some wrong keywords but a reasonable main plot), conflicting logic (wrong causal relation or temporal order), and chaotic scenes (difficult to understand). We can observe that unrelated entities/events and conflicting orders make up most of the errors for all the models. Compared with Fusion, GPT-2 (Fine-tune) reduces chaotic scenes effectively but still suffers from severe repetition. Equipped with external knowledge and multi-task learning, our model can further reduce chaotic logic and meanwhile avoid repetition. However, the analysis result illustrates that generating a coherent and reasonable story is challenging.