Utilizing Non-Parallel Text for Style Transfer by Making Partial Comparisons
Authors: Di Yin, Shujian Huang, Xin-Yu Dai, Jiajun Chen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments to compare our method to other existing approaches on two review datasets. Both automatic and manual evaluations show that our approach can significantly improve the performance of existing adversarial methods, and outperforms most state-of-the-art models. |
| Researcher Affiliation | Academia | Di Yin, Shujian Huang , Xin-Yu Dai and Jiajun Chen National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China yind@smail.nju.edu.cn, {huangsj, daixinyu, chenjj}@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Making partial comparisons |
| Open Source Code | Yes | Our code and data will be available on Github1. ... 1https://github.com/yd1996/Partial Comparison |
| Open Datasets | Yes | Datasets We conduct experiments on the Yelp review dataset and Amazon review dataset (Table 1) released by [Li et al., 2018a] |
| Dataset Splits | Yes | Table 1: Statistics of the datasets. ... Dataset Attributes Train Dev Test ... Hyper-parameters are selected based on the validation set |
| Hardware Specification | Yes | We implement our model based on Py Torch4 and use four NVIDIA GTX1080Ti graphic cards for learning. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Word2Vec' but does not specify their exact version numbers. It also refers to optimization algorithms like Adam and RMSprop, which are not software dependencies with specific versions. |
| Experiment Setup | Yes | The encoder E and the generator G are single-layer LSTMRNNs with input dimension of 300 and hidden dimensions of 350. The dimension of style embedding is 50. We use a batch size of 160, which contains 80 samples from X and Y respectively. Hyper-parameters are selected based on the validation set, and we use grid search to pick the best parameters. The learning rate is selected from [1e 4, 2e 4, 5e 4, 1e 3], and the weights of each term in the training objective (λz, λc, λs, λlm) are all selected from [0.1, 0.5, 1.0, 2.0, 5.0]. |