Optimal and efficient text counterfactuals using Graph Neural Networks
<|reference_start|>Optimal and efficient text counterfactuals using Graph Neural Networks: As NLP models become increasingly integral to decision-making processes, the need for explainability and interpretability has become paramount. In this work, we propose a framework that achieves the aforementioned by generating semantically edited inputs, known as counterfactual interventions, which change the model prediction, thus providing a form of counterfactual explanations for the model. We test our framework on two NLP tasks - binary sentiment classification and topic classification - and show that the generated edits are contrastive, fluent and minimal, while the whole process remains significantly faster that other state-of-the-art counterfactual editors.<|reference_end|>
@article{lymperopoulos2024optimal,
    title={Optimal and efficient text counterfactuals using Graph Neural Networks},
    author={Dimitris Lymperopoulos, Maria Lymperaiou, Giorgos Filandrianos,
  Giorgos Stamou},
    journal={arXiv preprint arXiv:2408.01969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01969},
    primaryClass={cs.CL}
}