Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Visual Dialogue State Tracking for Question Generation
Authors: Wei Pang, Xiaojie Wang11831-11838
AAAI 2020 | Venue PDF | 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 EMAIL |
| 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. |