Style Transfer in Text: Exploration and Evaluation
Authors: Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark our models and the evaluation metrics on two style transfer tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with similar content preservation score but higher style transfer strength comparing to autoencoder. |
| Researcher Affiliation | Academia | Zhenxin Fu,1 Xiaoye Tan,1 Nanyun Peng,2 Dongyan Zhao,1,3 Rui Yan1,3 1Institute of Computer Science and Technology, Peking University, Beijing, China 2Information Science Institute, University of Southern California, California, USA 3Beijing Institute of Big Data Research, Beijing, China {fuzhenxin, txye, zhaodongyan, ruiyan}@pku.edu.cn, npeng@isi.edu |
| Pseudocode | No | The paper describes its models and algorithms in prose and mathematical equations but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions that a dataset is |
| Open Datasets | Yes | We compose a dataset1 of paper-news titles to facilitate the research in language style transfer. We composed the first dataset ourselves and used the data released by He and Mc Auley (2016) as the second dataset. 1Available at https://github.com/fuzhenxin/textstyletransferdata |
| Dataset Splits | Yes | For both datasets, we divided them into three parts: training, validation, and test data. The size of the validation and test data is 2,000 sentences, and the rest are used as training data. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. It does not mention any specific GPU models, CPU models, or other computing resources. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | We use Adadelta (Zeiler 2012) with the initial learning rate 0.0001 and batch size 128 to learn the parameters for all models. The best parameters are decided based on the perplexity on the validation data with a maximum of 50 training epochs for paper-news task and 10 training epochs for positive-negative task. For paper-news title transfer, we explored word embedding size of 64, encoder hidden vector size among {32,64,128}, and style embedding size among {32,64,128}. For positive-negative review transfer, we explored word embedding size of 64 for multi-decoder and {64,128} for style-embedding model, encoder hidden vector size among {16,32,64}, and style embedding size among {16,32,64}. |