Papers with Platt-Binning
Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for posterior calibration return uncalibrated estimations of class posteriors, thus leading to poorer generalization. |
| Approach: | They propose an end-to-end trained calibrator that directly optimizes the objective while minimizing the difference between predicted and empirical posterior probabilities. |
| Outcome: | The proposed calibrator reduces calibration error and improves performance on benchmark NLP classification tasks. |