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..
Latent Template Induction with Gumbel-CRFs
Authors: Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander Rush
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our methods with experiments on data-to-text generation and unsupervised paraphrase generation. |
| Researcher Affiliation | Collaboration | Yao Fu1, , Chuanqi Tan2, Bin Bi2, Mosha Chen2, Yansong Feng3, Alexander M. Rush4 1ILCC, University of Edinburgh, 2Alibaba Group, 3WICT, Peking Univeristy, 4Cornell University |
| Pseudocode | Yes | Algorithm 1 Forward Filtering Backward Sampling, Algorithm 2 Gumbel-CRF (Forward Filtering Backward Sampling with Gumbel-Softmax) |
| Open Source Code | Yes | Our code is available at https://github.com/FranxYao/Gumbel-CRF. |
| Open Datasets | Yes | For text modeling and data-to-text generation, we use the E2E dataset[50]... For paraphrase generation we follow the same setting as Fu et al. [16], and use the common MSCOCO dataset. |
| Dataset Splits | Yes | This dataset contains approximately 42K training, 4.6K validation and 4.6K testing sentences. |
| Hardware Specification | Yes | Models tested on Nvidia P100 with batch size 100. |
| Software Dependencies | No | The paper mentions various parameters for tuning (e.g., 'h entropy regularization, τ temperature annealing') but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | At the beginning of training, to prevent the decoder from ignoring z, we apply word dropout [7]... For optimization, we add a β coefficient to the entropy term, as is in the β-VAE [23]... Models tested on Nvidia P100 with batch size 100. |