Papers with balanced training
Fˆ2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text generation do not fully reflect the rich diversity of human language. |
| Approach: | They propose to use F2-Softmax and MefMax to train a balanced frequency distribution using a frequency class-based method. |
| Outcome: | The proposed methods improve the diversity and quality of generated texts. |
Balancing out Bias: Achieving Fairness Through Balanced Training (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to reducing group bias do not account for correlations between author demographics and linguistic variables, limiting their effectiveness. |
| Approach: | They extend a method for countering group bias using balanced training by balancing each demographic group in training and using protected attributes as input. |
| Outcome: | The proposed model outperforms all other methods when combined with balanced training. |