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..
Conditional Generative Adversarial Networks for Commonsense Machine Comprehension
Authors: Bingning Wang, Kang Liu, Jun Zhao
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods. |
| Researcher Affiliation | Academia | 1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China 2 University of Chinese Academy of Sciences, Beijing, 100049, China EMAIL |
| Pseudocode | Yes | Algorithm 1 Conditional Generative Adversarial Networks |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of the source code for the methodology described in this paper. It only links to third-party datasets/resources. |
| Open Datasets | Yes | Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application... We pre-train our CGANs in the New York Times (NYT) news article corpus3. 3https://catalog.ldc.upenn.edu/LDC2008T19 |
| Dataset Splits | No | The paper mentions "Validation Set" in Table 1 and discusses training/testing periods, but it does not explicitly provide the specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. It only mentions general training parameters. |
| Software Dependencies | No | The paper mentions using "word2vec" but does not specify version numbers for any software libraries, frameworks, or dependencies used in their implementation. |
| Experiment Setup | Yes | All weight and attention matrices are initiated by ο¬xing their largest singular values to 1.0. We use Adadelta with Ο = 0.999 to update parameter. We use L1 criteria with weight 1e-5 to regulate the parameter. All training process is implemented with batch size equals to 32. For the discriminator: we set the vocabulary size to 25000... The sentence GRU hidden state size is set to 128 and the document hidden state size is set to 150. For the generator: the decoder size is set to 256... Ξ΅ is set to 1.0E-20. The THRESHOLD was set to 0.2. For kd and kg, we truncate their max value to 20... |