text: Quality: The work focuses on a novel problem of generating text sample using GAN and a novel in-filling mechanism of words.  Using GAN to generate samples in adversarial setup in texts has been limited due to the mode collapse and training instability issues.  As a remedy to these problems an in-filling-task conditioning on the surrounding text has been proposed.  But, the use of the rewards at every time step (RL mechanism) to employ the actor-critic training procedure could be challenging computationally challenging. Clarity: The mechanism of generating the text samples using the proposed methodology has been described clearly.  However the description of the reinforcement learning step could have been made a bit more clear. Originality: The work indeed use a novel mechanism of in-filling via a conditioning approach to overcome the difficulties of GAN training in text settings.  There has been some work using GAN to generate adversarial examples in textual context too to check the robustness of classifiers.  How this current work compares with the existing such literature? Significance: The research problem is indeed significant  since the use of GAN in generating adversarial examples in image analysis has been more prevalent compared to text settings.  Also, the proposed actor-critic training procedure via RL methodology is indeed significant from its application in natural language processing. pros: (a) Human evaluations applications to several datasets show the usefulness of MaskGen over the maximum likelihood trained model in generating more realistic text samples. (b) Using a novel in-filling procedure to overcome the complexities in GAN training. (c) generation of high quality samples even with higher perplexity on ground truth set. cons: (a) Use of rewards at every time step to the actor-critic training procure could be computationally expensive. (b) How to overcome the situation where in-filling might introduce implausible text sequences with respect to the surrounding words? (c) Depending on the Mask quality GAN can produce low quality samples.  Any practical way of choosing the mask?