Papers by Estrid He
Effects of Human Adversarial and Affable Samples on BERT Generalization (2023.findings-emnlp)
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| Challenge: | Limiting quantities of training data is considered a key impediment to achieving generalizability in machine learning. |
| Approach: | They examine the impact of training data quality, not quantity, on a model’s generalizability by comparing human-adversarial and human-affable training samples. |
| Outcome: | The proposed model performance improves with 10-30% h-adversarial instances in text classification and relation extraction tasks. |