Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |