DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
Authors: Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha, Sunghun Kim
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two popular datasets show that Dialog WAE outperforms the state-of-the-art approaches in generating more coherent, informative and diverse responses. |
| Researcher Affiliation | Collaboration | Hong Kong University of Science and Technology, New York Universidy, Clova AI Research, NAVER |
| Pseudocode | Yes | Algorithm 1: Dialog WAE Training (UEnc: utterance encoder; CEnc: context encoder; Rec Net: recognition network; Pri Net: prior network; Dec: decoder) K=3, ncritic=5 in all experiments |
| Open Source Code | No | The paper does not provide an explicit statement or link to the open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our model on two dialogue datasets, Dailydialog (Li et al., 2017b) and Switchboard (Godfrey and Holliman, 1997), which have been widely used in recent studies (Shen et al., 2018; Zhao et al., 2017). |
| Dataset Splits | Yes | The datasets are separated into training, validation, and test sets with the same ratios as in the baseline papers, that is, 2316:60:62 for Switchboard (Zhao et al., 2017) and 10:1:1 for Dailydialog (Shen et al., 2018), respectively. |
| Hardware Specification | No | The paper mentions that models are 'fine-tuned with NAVER Smart Machine Learning (NSML) platform', but does not specify any hardware details like CPU, GPU models, or memory for the experimental setup. |
| Software Dependencies | Yes | All the models are implemented with Pytorch 0.4.03, and fine-tuned with NAVER Smart Machine Learning (NSML) platform (Sung et al., 2017; Kim et al., 2018). |
| Experiment Setup | Yes | The models are trained with mini-batches containing 32 examples each in an end-to-end manner. In the AE phase, the models are trained by SGD with an initial learning rate of 1.0 and gradient clipping at 1 (Pascanu et al., 2013). We decay the learning rate by 40% every 10th epoch. In the GAN phase, the models are updated using RMSprop (Tieleman and Hinton) with fixed learning rates of 5 10 5 and 1 10 5 for the generator and the discriminator, respectively. We tune the hyper-parameters on the validation set and measure the performance on the test set. |