Controllable Neural Story Plot Generation via Reward Shaping
Authors: Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, Mark O. Riedl
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques. |
| Researcher Affiliation | Academia | 1School of Interactive Computing, Georgia Institute of Technology 2Department of Computer Science, University of Kentucky {ptambwekar3, murtaza.d.210, ljmartin, animesh.mehta}@gatech.edu, harrison@cs.uky.edu, riedl@cc.gatech.edu |
| Pseudocode | No | The paper does not include a pseudocode block or a clearly labeled algorithm. |
| Open Source Code | No | The paper does not provide a statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We use the CMU movie summary corpus [Bamman et al., 2013]. ... The romance corpus was split into 90% training, and 10% testing data. |
| Dataset Splits | No | The paper states 'The romance corpus was split into 90% training, and 10% testing data.' but does not specify a separate validation split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models) used for its experiments. |
| Software Dependencies | No | The paper mentions using 'Tensorflow' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | Both the encoder and the decoder comprised of LSTM units, with a hidden layer size of 1024. The network was pre-trained for a total of 200 epochs using minibatch gradient descent and batch size of 64. |