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Register a new userTowards Improving Adversarial Training of NLP Models
نحو تحسين التدريب الخصم لنماذج NLP
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تدريب الخصم، طريقة لتعلم الشبكات العصبية العميقة القوية، تضم أمثلة خصومة أثناء التدريب. ومع ذلك، فإن الأساليب الحديثة لتوليد أمثلة allp adversarial تنطوي على البحث عن الفروضي وترميز الجملة باهظة الثمن لتقييد الحالات التي تم إنشاؤها. نتيجة لذلك، لا يزال يمثل تحديا لاستخدام التدريب المشدود الفانيليا لتحسين أداء نماذج NLP، والفوائد غير مرئية بشكل أساسي. تقترح هذه الورقة عملية تدريبية بسيطة ومحسنة من الفانيليا العدائية لنماذج NLP، والتي نستها المهاجمة على التدريب (A2T). الجزء الأساسي من A2T هو هجوم استبدال كلمة جديدة وأرخص محسن لتدريب الفانيليا الخصم. نحن نستخدم A2T لتدريب برت ونماذج روبرتا على مجموعة بيانات IMDB والطماطم الفاسدة والشبكة الصلبة و SNLI. تظهر نتائجنا تجريبيا أنه من الممكن تدريب نماذج NLP قوية باستخدام خصم أرخص بكثير. نوضح أن التدريب الصادق للفانيليا مع A2T يمكن أن يحسن متانة نموذج NLP للهجوم الذي تم تدريبه في الأصل مع النموذج الذي يدافع عنه أيضا ضد أنواع أخرى من هجمات استبدال الكلمات. علاوة على ذلك، نظهر أن A2T يمكن أن تحسن الدقة القياسية لنماذج NLP وتعميم المجال المتبادل والتفسيرية.
Adversarial training, a method for learning robust deep neural networks, constructs adversarial examples during training. However, recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. As a result, it remains challenging to use vanilla adversarial training to improve NLP models' performance, and the benefits are mainly uninvestigated. This paper proposes a simple and improved vanilla adversarial training process for NLP models, which we name Attacking to Training (A2T). The core part of A2T is a new and cheaper word substitution attack optimized for vanilla adversarial training. We use A2T to train BERT and RoBERTa models on IMDB, Rotten Tomatoes, Yelp, and SNLI datasets. Our results empirically show that it is possible to train robust NLP models using a much cheaper adversary. We demonstrate that vanilla adversarial training with A2T can improve an NLP model's robustness to the attack it was originally trained with and also defend the model against other types of word substitution attacks. Furthermore, we show that A2T can improve NLP models' standard accuracy, cross-domain generalization, and interpretability.
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