Papers with energy-based model
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work on controlled text generation has required attribute-based fine-tuning of the base language model or restricted the parameterization of the attribute discriminator. |
| Approach: | They propose a global score-based alternative for controllable text generation that combines arbitrary pre-trained black-box models for achieving desired attributes in the generated text. |
| Outcome: | The proposed method outperforms methods that require extra training or fine-tuning . the proposed method is based on a model with energy values of a linear combination of scores from black-box models . |
Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing calibration methods rescale posterior distributions of classifiers after training. |
| Approach: | They propose to use a noise contrastive estimation technique to train an energy-based model during finetuning of pretrained text encoders. |
| Outcome: | The proposed model can reach a better calibration competitive to strong baselines with little or no loss in accuracy. |
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (2021.acl-long)
Copied to clipboard
Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum
| Challenge: | Autoregressive neural machine translation (NMT) uses a tractable likelihood computation and efficient sampling. |
| Approach: | They propose to use an energy-based model to mimic the behavior of the task measure and use it to train an energy based re-ranking algorithm. |
| Outcome: | The proposed model improves on the samples drawn from the NMT with a higher BLEU score than the experimental model and the energy-based re-ranking algorithm. |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
Copied to clipboard
| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |