Automated Storytelling via Causal, Commonsense Plot Ordering
This work addresses the problem of generating coherent narratives for applications in creative writing or AI storytelling, but it is incremental as it builds on existing causal relation concepts.
The paper tackled automated story plot generation by introducing soft causal relations inferred from commonsense reasoning, resulting in the C2PO system that was evaluated against baselines to assess its impact on perceived story quality, with findings showing improved coherence in human studies.
Automated story plot generation is the task of generating a coherent sequence of plot events. Causal relations between plot events are believed to increase the perception of story and plot coherence. In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning. We demonstrate C2PO, an approach to narrative generation that operationalizes this concept through Causal, Commonsense Plot Ordering. Using human-participant protocols, we evaluate our system against baseline systems with different commonsense reasoning reasoning and inductive biases to determine the role of soft causal relations in perceived story quality. Through these studies we also probe the interplay of how changes in commonsense norms across storytelling genres affect perceptions of story quality.