Papers by Floris Bex
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. |
BERT, are you paying attention? Attention regularization with human-annotated rationales (2026.eacl-long)
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| Challenge: | Attention regularisation aims to supervise the attention patterns in language models like BERT. |
| Approach: | They compare regularisation on human rationales with random tokens to find that human-annotated rationale is better at reducing model sensitivity to spurious correlations. |
| Outcome: | The proposed regularisation method improves model performance and model robustness, but not with human-annotated rationales. |