Paper:
While there has been great interest on generating imperceptible adversarial examples in continuous data domain (e.g. image and audio) to explore the model vulnerabilities, generating adversarial text in the discrete domain is still challenging. The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP) tasks. In particular, we propose a tree based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation. A tree based decoder is then applied to ensure the grammar correctness of the generated text. It also enables the flexibility of making manipulations on different levels of text, such as sentence (AdvCodec(Sent)) and word (AdvCodec(Word)) levels. We consider multiple attacking scenarios, including appending an adversarial sentence or adding unnoticeable words to a given paragraph, to achieve arbitrary targeted attack. To demonstrate the effectiveness of the proposed method, we consider two most representative NLP tasks: sentiment analysis and question answering (QA). Extensive experimental results and human studies show that AdvCodec generated adversarial text can successfully attack the neural models without misleading the human. In particular, our attack causes a BERT-based sentiment classifier accuracy to drop from 0.703 to 0.006, and a BERT-based QA model's F1 score to drop from 88.62 to 33.21 (with best targeted attack F1 score as 46.54). Furthermore, we show that the white-box generated adversarial texts can transfer across other black-box models, shedding light on an effective way to examine the robustness of existing NLP models.

Review:
Motivated by recent development of attack/defense methods addressing the vulnerability of deep CNN classifiers for images, this paper proposes an attack framework for adversarial text generation, in which an autoencoder is employed to map discrete text to a high-dimensional continuous latent space, standard iterative optimization based attack method is performed in the continuous latent space to generate adversarial latent embeddings, and a decoder generates adversarial text from the adversarial embeddings.  Different generation strategies of perturbing latent embeddings at sentence level or masked word level are both explored. Adversarial text generation can take either a form of appending an adversarial sentence or a form of scattering adversarial words into different specified positions. Experiments on both sentiment classification and question answering show that the proposed attack framework outperforms some baselines. Human evaluations are also conducted.
Pros: 
This paper is well-written overall. Extensive experiments are performed.
Many human studies comparing different adversarial text generation strategies and evaluating adversarial text for sentiment classification/question answering are conducted.
Cons:
1) Although the studied problem in this paper is interesting, the technical innovation is very limited. All the techniques are standard or known. 
2) There are two major issues: lacking a rigorous metric of human unnoticeability and lacking justification of the advantage of the tree-based autoencoder. I think the first issue is a major problem that renders all the claims in this paper questionable. The metrics used to define adversarial images for deep CNN classifiers are indeed valid and produce unnoticeable images for human observers. But in this paper, the adversarial attack is performed in the latent embedding space, and there is no explicit constraint enforced on the output text. It’s unconvincing that this approach will generate adversarial text that seems negligible to humans. Therefore, the studied problem in this paper has a completely different nature from the one for CNN image classifiers and it is hard to convince readers that the proposed  framework generates adversarial text legitimate to human readers. 
3) It is unclear why tree-structured LSTM instead of a standard LSTM/GRU should be chosen in this framework for adversarial text generation. If this architecture is preferred, sufficient ablation studies should be conducted.
4) In section 3.3, the description about adversarial attacks at word level is unclear. More detailed loss function and algorithms along with equations should be provided.
5) In section 5.2, it is unclear that the majority answers on the adversarial text will, respectively, match the majority answers on the original text. Moreover, it seems that there is a large performance drop from original text to adversarial text. Therefore, it is valid to argue that whether the proposed framework can generate legitimate adversarial text to human readers or not.
6) It’s better to include many examples of generated adversarial text in the appendix.
7) Missing training details: It is unclear how the model architectures are chosen, and learning rate, optimizer, training epochs etc. are also missing. All these training details should be included in the appendix.
8) Minor: Figure 1: "Append an initial sentence...",  section 3: "map discrete text into a high dimensional...",  section 3.2.2: "Different from attacking sentiment analysis..." ....
In summary, the research direction of adversarial text generation studied in this paper is interesting and promising. However, some technical details are questionable, and the produced results without rigorous metrics seem to be unconvincing.