Venue: ICLR 2018 conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: Generating high-quality sentences/paragraphs is an open research problem that is receiving a lot of attention. This text generation task is traditionally done using recurrent neural networks. This paper proposes to generate text using GANs. GANs are notorious for drawing images of high quality but they have a hard time dealing with text due to its discrete nature. This paper's approach is to use an actor-critic to train the generator of the GAN and use the usual maximum likelihood with SGD to train the discriminator. The whole network is trained on the "fill-in-the-blank" task using the sequence-to-sequence architecture for both the generator and the discriminator. At training time, the generator's encoder computes a context representation using the masked sequence. This context is conditioned upon to generate missing words. The discriminator is similar and conditions on the generator's output and the masked sequence to output the probability of a word in the generator's output being fake or real. With this approach, one can generate text at test time by setting all inputs to blanks. 

Pros and positive remarks: 
--I liked the idea behind this paper. I find it nice how they benefited from context (left context and right context) by solving a "fill-in-the-blank" task at training time and translating this into text generation at test time. 
--The experiments were well carried through and very thorough.
--I second the decision of passing the masked sequence to the generator's encoder instead of the unmasked sequence. I first thought that performance would be better when the generator's encoder uses the unmasked sequence. Passing the masked sequence is the right thing to do to avoid the mismatch between training time and test time.

Cons and negative remarks:
--There is a lot of pre-training required for the proposed architecture. There is too much pre-training. I find this less elegant. 
--There were some unanswered questions:
            (1) was pre-training done for the baseline as well?
            (2) how was the masking done? how did you decide on the words to mask? was this at random?
            (3) it was not made very clear whether the discriminator also conditions on the unmasked sequence. It needs to but 
                  that was not explicit in the paper.
--Very minor: although it is similar to the generator, it would have been nice to see the architecture of the discriminator with example input and output as well.


Suggestion: for the IMDB dataset, it would be interesting to see if you generate better sentences by conditioning on the sentiment as well.

review_title: Very thorough empirical study

final_score: rating: 7: Good paper, accept
review_content: This paper proposes MaskGAN, a GAN-based generative model of text based on
the idea of recovery from masked text. 
For this purpose, authors employed a reinceforcement learning approach to
optize a prediction from masked text. Moreover, authors argue that the 
quality of generated texts is not appropriately measured by perplexities,
thus using another criterion of a diversity of generated n-grams as well as
qualitative evaluations by examples and by humans.

While basically the approach seems plausible, the issue is that the result is
not compared to ordinary LSTM-based baselines. While it is better than a 
conterpart of MLE (MaskedMLE), whether the result is qualitatively better than
ordinary LSTM is still in question.

In fact, this is already appearent both from the model architectures and the
generated examples: because the model aims to fill-in blanks from the text
around (up to that time), generated texts are generally locally valid but not
always valid globally. This issue is also pointed out by authors in Appendix
A.2. 
While the idea of using mask is interesting and important, I think if this
idea could be implemented in another way, because it resembles Gibbs sampling
where each token is sampled from its sorrounding context, while its objective
is still global, sentence-wise. As argued in Section 1, the ability of 
obtaining signals token-wise looks beneficial at first, but it will actually
break a global validity of syntax and other sentence-wise phenoma.

Based on the arguments above, I think this paper is valuable at least
conceptually, but doubt if it is actually usable in place of ordinary LSTM
(or RNN)-based generation.
More arguments are desirable for the advantage of this paper, i.e. quantitative
evaluation of diversity of generated text as opposed to LSTM-based methods.

*Based on the rebuttals and thorough experimental results, I modified the global rating.
review_title: Locally better, but globally not always better

final_score: rating: 7: Good paper, accept
review_content: 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?
review_title: The paper discusses a mechanism of generating text samples using GAN and a in-filling mechanism of missing words conditional on the surrounding text. I feel the Reinforcement learning idea that has been introduced to employ a actor-critic training procedure could be computationally challenging and the motivation to do so is not explicitly clear.

Meta-review: This paper makes progress on the open problem of text generation with GANs, by a sensible combination of novel approaches.   The method was described clearly, and is somewhat original.   The only problem is the hand-engineering of the masking setup.

Decision: Accept (Poster)