Latent Template Induction with Gumbel-CRFs
Authors: Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander Rush
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |