Visual Dialogue State Tracking for Question Generation
Authors: Wei Pang, Xiaojie Wang11831-11838
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Guess What?! dataset show that our model significantly outperforms existing methods and achieves new state-of-the-art performance. |
| Researcher Affiliation | Academia | Wei Pang, Xiaojie Wang Center for Intelligence Science and Technology, School of Computer Science, Beijing University of Posts and Telecommunication {pangweitf, xjwang}@bupt.edu.cn |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | Our code and other materials will be published in the near future. |
| Open Datasets | Yes | We evaluate our model on the Guess What?! dataset introduced in (de Vries et al. 2017). |
| Dataset Splits | Yes | We use the standard partition of the dataset to the training (70%), validation (15%) and test (15%) set as in (de Vries et al. 2017; Strub et al. 2017). |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory specifications used for experiments. |
| Software Dependencies | No | The paper mentions software components like 'Faster-RCNN', 'LSTM', 'Adam optimizer', 'REINFORCE', 'VGG network', 'Res Net152', 'swish activation', but does not provide specific version numbers for these or other libraries/frameworks. |
| Experiment Setup | Yes | We train the Guesser and Oracle model for 30 epochs, and pre-train the QGen model for 50 epochs, using Adam optimizer (Kingma and Ba 2015) with a learning rate of 1e-4 and a batch size of 64. ... post-train the QGen model with REINFORCE (Williams 1992; Sutton et al. 2000) for 500 epochs, using stochastic gradient descent (SGD) with a learning rate of 1e-3 and a batch size of 64. |