Automated Storytelling via Causal, Commonsense Plot Ordering
By Prithviraj Ammanabrolu, W. Cheung, William Broniec, Mark O. Riedl. 2020
73 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yejin Choi
- Hannaneh Hajishirzi
- Ashish Sabharwal
- Daniel Khashabi
- Peter Clark
- Tushar Khot
- Mohit Bansal
- Kyle Richardson
In Natural Language Processing
- Yejin Choi
- Ashish Sabharwal
- Hannaneh Hajishirzi
- Daniel Khashabi
- Tushar Khot
- Peter Clark
- Ronan Le Bras
- Kyle Richardson
Citations in the corpus
- Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2021, background): “, 2020, 2021), story telling (Ammanabrolu et al., 2020) and fantasy games (Ammanabrolu et al.”
- COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs (2020, background): “This flexibility allows them to be used out-of-the-box, and they have been applied to new, previously unexplored tasks, such as sarcastic comment generation (Chakrabarty et al. 2020), therapy chatbots (Kearns et al. 2020), and automated story plot generation (Ammanabrolu et al. 2020).”
- Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning (2021, methodology): “These questions have been used in a number of other story generator evaluations (Purdy et al., 2018; Tambwekar et al., 2019; Ammanabrolu et al., 2020a, 2021; Castricato et al., 2021; Peng et al., 2021).”
- Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning (2022, methodology): “based—capable of producing stories (Roemmele, 2016; Khalifa et al., 2017; Martin et al., 2018; Clark et al., 2018; Fan et al., 2018; Ammanabrolu et al., 2021).”
- Temporal Reasoning on Implicit Events from Distant Supervision (2020, background): “Comparatively, our work captures longer-range dependencies in narrative text (for related ideas, see Ammanabrolu et al. (2021)).”