Text generation is a critical and difficult natural language processing task. Maximum likelihood estimate (MLE) based models have been arguably suffered from exposure bias in the inference stage and thus varieties of language generative adversarial networks (GANs) bypassing this problem have emerged. However, recent study has demonstrated that MLE models can constantly outperform GANs models over quality-diversity space under several metrics. In this paper, we propose a quality-diversity controllable language GAN. We illustrate an intimate connection between forward/reverse KL divergences and our principled approach, and demonstrate that our proposed model can achieve quality-diversity control by adjusting a hyper-parameter. Through extensive experiments, we show that the proposed model outperforms MLE models and other GANs variants on synthetic data and is more competitive on real-world tasks such as COCO Image Captions and EMNLP2017 WMT News datasets.
This paper provides a method (loss function) for training GAN model for generation of discrete text token generation. The aim of this loss method to control the trade off between quality vs diversity while generating the text data.
For example,
if original sentence is "The company ’ s shares rose 1 . 5 percent to 1 . 81 percent , the highest since the end of the year ." and the output is "The company ’ s shares rose 1 . 5 percent to 1 . 81 percent , the highest since the end of the year ." then the quality of generation is high and diversity is low. 
Pros:
1. The paper is very well written, with importance to the smaller details. It is a very good read for even people who are new to this problem. Especially, I appreciate the part where authors took efforts in writing why a few metrics are not used!
2. The motivation is good and also contributions are explicitly written. The details of the approach are provided clearly.
3. The experiments are provided in two different datasets and also the experiments support the two major claims in the paper.
Cons:
1. The primary concern with this submission is the novelty. The Proposition 1 of using forward-backward JSD based divergence has already been proposed in Li et al. (2019). Also, Li et al. (2019) proposes the entire contribution of this paper. The only difference is the introduction of \pi, which controls the percentage of labelled data to be considered between the generated data and original data. Basically, Li et al. (2019) is a specific case of this paper where \pi = 0.5. Thus, I would consider this paper as one additional experiment in Li et al. (2019) and not a whole paper as such.
2. Also, in the formulation in Eqn 2, the proposition 2 becomes a direct observation when \pi becomes 0 or \pi becomes 1. I would not call this as a proposition but a mere observation of Eqn 2.
3. Thus, taking away proposition 1 (already proposed in Li et al. (2019)) and proposition 2 (which is a mere observation) I do not find any novelty in this paper.
4. From an experiments perspective, Li et al. (2019) performed experiments in 4 datasets: Chinese Poems, MS COCO captions, Obama Speech, and EMNLP2017 WMT news. However, in this paper, results are shown in only two datasets - MS COCO captions and EMNLP2017 WMT news. Was it because that this paper was submitted in haste and/or the results in the other two datasets are not compelling enough to share?
5. The result analysis are poor - the authors have shown only the numbers in the tables, while the interpretation on these numbers and the discussion is left to reviewers discretion. Also, there are no generated examples that the authors are showing in either of the datasets. The authors should further discuss and analyze the results, show generated examples, and explain success and failure cases and the reasons behind them.
Overall, I find the novelty and the experimental analysis of the paper, very weak.