Automated Storytelling via Causal, Commonsense Plot Ordering
Authors: Prithviraj Ammanabrolu, Wesley Cheung, William Broniec, Mark O. Riedl5859-5867
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using human-participant protocols, we evaluate our system against baseline systems with different commonsense reasoning approaches 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. |
| Researcher Affiliation | Academia | Prithviraj Ammanabrolu, Wesley Cheung, William Broniec, and Mark O. Riedl Georgia Institute of Technology {raj.ammanabrolu,wcheung8,d.tu,wbroniec3,riedl}@gatech.edu |
| Pseudocode | No | The paper describes the system architecture and processes with text and figures, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code found at https://github.com/rajammanabrolu/C2PO. |
| Open Datasets | Yes | We evaluate on a story dataset with two genres mystery stories and fairy tales first introduced by Ammanabrolu et al. (2020a)3. Footnote 3: https://github.com/rajammanabrolu/World Generation |
| Dataset Splits | No | The paper states: “The data is partitioned into train and test splits in a 8:2 ratio” but does not explicitly mention a separate validation set split or its details. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions several software tools and models like BERT, COMET, fairseq, and Open IE, but it does not provide specific version numbers for these software components or any other libraries like Python, PyTorch, or TensorFlow versions, which are needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the overall experimental design, the human evaluation setup, and the statistical tests performed. However, it does not provide specific numerical hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations for the models used in the experiments. |