Adversarial Text Generation via Feature-Mover's Distance
Authors: Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, Lawrence Carin
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. |
| Researcher Affiliation | Collaboration | 1Duke University, 2Microsoft Dynamics 365 AI Research, 3Microsoft Research, 4Baidu Research |
| Pseudocode | Yes | Algorithm 1 IPOT algorithm [59] and Algorithm 2 Adversarial text generation via FMD. |
| Open Source Code | No | Our code will be released to encourage future research. |
| Open Datasets | Yes | CUB captions [57], MS COCO captions [38], and EMNLP2017 WMT News [24]. For unsupervised decipher task, we adapt the idea of feature mover s distance to the original framework of Cipher GAN and test this modified model on the Brown English text dataset [16] referencing The Brown English-language corpus [30]. |
| Dataset Splits | No | Table 1 lists 'Train' and 'Test' dataset sizes but does not provide details on validation splits or percentages. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | Algorithm 2 lists 'batch size n, learning rate η, maximum number of iterations N' as inputs. Additionally, for conditional tasks, 'λ is a hyperparameter that balances these two terms'. |