MASKGAN: BETTER TEXT GENERATION VIA FILLING IN THE
Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maximum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model.
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.