We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to minimally alter the text to change the model's prediction. White-box approaches have been successfully applied to similar problems in vision where one can directly optimize the continuous input. Optimization-based approaches become difficult in the language domain due to the discrete nature of text. We bypass this issue by directly optimizing in the latent space and leveraging a language model to generate candidate modifications from optimized latent representations. We additionally use Shapley values to estimate the combinatoric effect of multiple changes. We then use these estimates to guide a beam search for the final counterfactual text. We achieve favorable performance compared to recent white-box and black-box baselines using human and automatic evaluations. Ablation studies show that both latent optimization and the use of Shapley values improve success rate and the quality of the generated counterfactuals.

本文解决了生成对抗文本的问题，从而理解和调试分类器，通过优化潜在空间并利用语言模型生成候选修改来绕过离散性文本的优化难题，并使用 Shapley 值估计多个更改的组合效果来确保结果，结果显示潜在空间优化和使用 Shapley 值都显著提高了生成的反事实文本的成功率和质量。

通过隐式优化和 Shapley 引导搜索生成对抗样本