Residual Energy-Based Models for Text Generation

This repo implements the Residual Energy-Based Model as described in Residual Energy-Based Models for Text Generation. Note that both data and model (generator+discriminator) are different from the original paper: the goal is just to show how the pipeline works, using much less computational resources.

The training and evaluation scripts can be found in REBM.ipynb. We suggest opening it using Google Colab with a GPU instance.

Method and related research

Energy-based text generation: residual models, resampling and diffusion explains the sequence-level energy formulation, noise-contrastive estimation, and importance resampling, with an interactive reweighting example and connections to energy-based diffusion language models.

Citation

@inproceedings{deng2020residual,
  title={Residual Energy-Based Models for Text Generation},
  author={Deng, Yuntian and Bakhtin, Anton and Ott, Myle and Szlam, Arthur and Ranzato, Marc'Aurelio},
  booktitle={International Conference on Learning Representations},
  year={2020}
}

@article{bakhtin2021residual,
  title={Residual Energy-Based Models for Text},
  author={Bakhtin, Anton and Deng, Yuntian and Gross, Sam and Ott, Myle and Ranzato, Marc'Aurelio and Szlam, Arthur},
  journal={Journal of Machine Learning Research},
  volume={22},
  number={40},
  pages={1--41},
  year={2021}
}