Papers with ML systems
Influence Scores at Scale for Efficient Language Data Sampling (2023.emnlp-main)
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| Challenge: | Recent studies have shown that ML models can be fine-tuned on as much data as possible without degradation in performance metrics. |
| Approach: | They evaluate the applicability of influence scores in language classification tasks by random sampling and stress-testing one of the scores. |
| Outcome: | The proposed model can be fine-tuned on 50% of the original data without degradation in performance metrics. |
Generating Realistic Natural Language Counterfactuals (2021.findings-emnlp)
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| Challenge: | Existing methods to explain ML tasks for natural language text are either unrealistic or introduce imperceptible changes. |
| Approach: | They propose a method that combines a conditional GAN and embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks. |
| Outcome: | The proposed method outperforms baseline methods on fidelity and human judgments of naturalness across multiple datasets and multiple predictive models. |