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Register a new userText Counterfactuals via Latent Optimization and Shapley-Guided Search
النص مضاد للتصرف عبر التحسين الكامن البحث في SHOPLEY
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ندرس مشكلة توليد نص موادي لتصنيف كوسيلة لفهم وتصحيح التصحيح. بالنظر إلى إدخال نصي ونموذج تصنيف، نهدف إلى تغيير النص الصغير لتغيير تنبؤ النموذج. تم تطبيق نهج الصندوق الأبيض بنجاح على مشاكل مماثلة في الرؤية حيث يمكن للمرء تحسين المدخلات المستمرة مباشرة. تصبح النهج القائمة على التحسين صعبة في مجال اللغة بسبب الطبيعة المنفصلة للنص. نحن نتجاوز هذه المشكلة من خلال تحسين مباشرة في المساحة الكامنة والاستفادة من نموذج لغة لإنشاء تعديلات مرشحة من تمثيلات كامنة محسنة. نحن بالإضافة إلى استخدام قيم SHOPLEY لتقدير تأثير COMPINATIC من التغييرات المتعددة. ثم نستخدم هذه التقديرات لتوجيه بحث الشعاع عن النص النهائي العالمي. نحن نحقق أداء مواتية مقارنة بالبضعة البيضاء الحديثة والسود الأسود باستخدام التقييمات البشرية والآلية. تشير دراسات الاجتثاث إلى أن كل من الأمثل الكامنة واستخدام قيم shabley يحسن معدل النجاح ونوعية الوسائل المتعددة الناتجة.
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.
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