ALT: An Automatic System for Long Tail Scenario Modeling

In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and computing resources for model inference stage. This problem is widely encountered in various applications, yet has received deficient attention so far. We present an automatic system named ALT to deal with this problem. Several efforts are taken to improve the algorithms used in our system, such as employing various automatic machine learning related techniques, adopting the meta learning philosophy, and proposing an essential budget-limited neural architecture search method, etc. Moreover, to build the system, many optimizations are performed from a systematic perspective, and essential modules are armed, making the system more feasible and efficient. We perform abundant experiments to validate the effectiveness of our system and demonstrate the usefulness of the critical modules in our system. Moreover, online results are provided, which fully verified the efficacy of our system.

Generating Natural Language Adversarial Examples on a Large Scale with Generative Models

Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods generate adversarial texts in a pair-wise way, that is, an adversarial text can only be created from a real-world text by replacing a few words. In many applications, these texts are limited in numbers, therefore their corresponding adversarial examples are often not diverse enough and sometimes hard to read, thus can be easily detected by humans and cannot create chaos at a large scale. In this paper, we propose an end to end solution to efficiently generate adversarial texts from scratch using generative models, which are not restricted to perturbing the given texts. We call it unrestricted adversarial text generation. Specifically, we train a conditional variational autoencoder (VAE) with an additional adversarial loss to guide the generation of adversarial examples. Moreover, to improve the validity of adversarial texts, we utilize discrimators and the training framework of generative adversarial networks (GANs) to make adversarial texts consistent with real data. Experimental results on sentiment analysis demonstrate the scalability and efficiency of our method. It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.