text: Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite. In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. To address the challenges caused by the discrete input space, we propose a projected gradient method combined with group lasso and gradient regularization. To handle the almost infinite output space, we design some novel loss functions to conduct non-overlapping attack and targeted keyword attack. We apply our algorithm to machine translation and text summarization tasks, and verify the effectiveness of the proposed algorithm: by changing less than 3 words, we can make seq2seq model to produce desired outputs with high success rates. On the other hand, we recognize that, compared with the well-evaluated CNN-based classifiers, seq2seq models are intrinsically more robust to adversarial attacks.
  @cite_16: While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the representation, such as input word-vector dimensions, intermediate hidden units, or input words. We present several approaches to analyzing the effects of such erasure, from computing the relative difference in evaluation metrics, to using reinforcement learning to erase the minimum set of input words in order to flip a neural model's decision. In a comprehensive analysis of multiple NLP tasks, including linguistic feature classification, sentence-level sentiment analysis, and document level sentiment aspect prediction, we show that the proposed methodology not only offers clear explanations about neural model decisions, but also provides a way to conduct error analysis on neural models.
summary: Although lots of works have been done to fool CNN-based models, only a few studies have examined RNN-based models, and the majority of them focus on simple text classification problems. first uses Fast Gradient Sign Method (FGSM) to conduct an attack on RNN LSTM-based classification problems. In order to generate text adversarial examples, @cite_16 proposes to use reinforcement learning to locate important words that could be deleted in sentiment classification. and generate adversarial sequences by inserting or replacing existing words with typos and synonyms. aims to attack sentiment classification models in a black-box setting. It develops some scoring functions to find the most important words to modify. These approaches differ from our work in that they study simple text classification problems while we focus on the more challenging seq2seq model with sequential outputs. Other than attacking text classifiers, aims to fool reading comprehension systems by adding misleading sentences, which has a different focus than ours. uses the generative adversarial network (GAN) to craft natural adversarial examples. However, it can only perform the untargeted attack and also suffers from high computational cost.