Plan-and-Write: Towards Better Automatic Storytelling
Authors: Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, Rui Yan7378-7385
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations. |
| Researcher Affiliation | Collaboration | Lili Yao,1,3 Nanyun Peng,2 Ralph Weischedel,2 Kevin Knight,2 Dongyan Zhao,1 Rui Yan1 liliyao@tencent.com, {npeng,weisched,knight}@isi.edu {zhaodongyan,ruiyan}@pku.edu.cn 1Institute of Computer Science and Technology, Peking University 2Information Sciences Institute, University of Southern California, 3Tencent AI Lab |
| Pseudocode | No | The paper describes mathematical formulations and model architectures but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and appendix will be available at https://bitbucket.org/Violet Peng/language-model |
| Open Datasets | Yes | We conduct the experiments on the ROCStories corpus (Mostafazadeh et al. 2016a). |
| Dataset Splits | Yes | We split the original training data into 8:1:1 for training, validation, and testing. |
| Hardware Specification | No | The paper does not specify any hardware details such as specific GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions neural generation models and SGD but does not provide specific version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | We train all the models using stochastic gradient descent (SGD). For the encoder and decoder in our generation models, we tune the hyper-parameters of the embedding and hidden vector dimensions and the dropout rate by grid search. We randomly initialize the word embeddings and tune the dimensions in the range of [100, 200, 300, 500] for storyline generation and [300, 500, 1000] for story generation. We tune the hidden vector dimensions in the range of [300, 500, 1000]. The embedding and hidden vector dropout rates are all tuned from 0 to 0.5, step by 0.1. We tune all baselines and proposed models based on BLEU scores (Papineni et al. 2002) on the validation set. Details of the best hyper-parameter values for each setting are given in Appendix. |