TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation
Authors: Chun-Hsing Lin, Siang-Ruei Wu, Hung-yi Lee, Yun-Nung Chen
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate that Taylor GAN achieves state-of-the-art performance without maximum likelihood pre-training. The dataset used in the experiments is EMNLP 2017 News... We evaluate our model s ability to generate realistic and diverse texts with n-gram based metric, Fréchet Embedding Distance and language model based metric. |
| Researcher Affiliation | Academia | Chun-Hsing Lin Siang-Ruei Wu Hung-Yi Lee Yun-Nung Chen National Taiwan University, Taipei, Taiwan |
| Pseudocode | No | The paper describes the proposed method verbally and mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and data are available at https://github.com/Miu Lab/Taylor GAN/ |
| Open Datasets | Yes | The dataset used in the experiments is EMNLP 2017 News, where sentences have a maximum length of 50 tokens and a vocabulary of 5.3k words after performing lowercase. The data is at https://github.com/pclucas14/Gans Falling Short/tree/master/real_data_experiments/data/news |
| Dataset Splits | Yes | Training and validation data consist of 269k and 10k sentences respectively. |
| Hardware Specification | Yes | Each training takes approximately 1 day on Nvidia Tesla V100 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or other libraries). |
| Experiment Setup | Yes | All models (detailed in Appendix E) are trained for at most 200 epochs, namely 800k training steps with the batch size N = 64. |