DelibGAN: Coarse-to-Fine Text Generation via Adversarial Network
In this paper, we propose a novel adversarial learning framework, namely DelibGAN, for generating high-quality sentences without supervision. Our framework consists of a coarse-to-fine generator, which contains a first-pass decoder and a second-pass decoder, and a multiple instance discriminator. And we propose two training mechanisms DelibGAN-I and DelibGAN-II. The discriminator is used to fine-tune the second-pass decoder in DelibGAN-I and further evaluate the importance of each word and tune the first-pass decoder in DelibGAN-II. We compare our models with several typical and state-of-the-art unsupervised generic text generation models on three datasets (a synthetic dataset, a descriptive text dataset and a sentimental text dataset). Both qualitative and quantitative experimental results show that our models produce more realistic samples, and DelibGAN-II performs best.
 This paper proposes using a deliberation network approach to text generation with GANs. It does this by using two decoders where the discriminator is trained on the second decoder and signals from that training are also used to improve the first decoder. Since both approaches have been published before (deliberation networks and policy-gradient based GAN training), the novelty seems to be limited. Additionally, the experiments appear to mostly be focused on single sentence text generation, which is usually not an issue for maximum likelihood trained models. The poor samples from DelibMLE appear much worse than typical maximum likelihood trained language models. Since the model sizes and hyperparameters are not given, it*s possible this is due to an overly small model. The lack of a detailed description of the model hyperparameters would also make this work very hard to reproduce. There*s also very little description of the datasets, for example, the length of a typical example. Finally, while the human evaluation results are interesting, the lack of standard deviations make it hard to measure the significance of the results, especially when only 100 sentences from each model is rated.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DelibGAN: Coarse-to-Fine Text Generation via Adversarial Network
In this paper, we propose a novel adversarial learning framework, namely DelibGAN, for generating high-quality sentences without supervision. Our framework consists of a coarse-to-fine generator, which contains a first-pass decoder and a second-pass decoder, and a multiple instance discriminator. And we propose two training mechanisms DelibGAN-I and DelibGAN-II. The discriminator is used to fine-tune the second-pass decoder in DelibGAN-I and further evaluate the importance of each word and tune the first-pass decoder in DelibGAN-II. We compare our models with several typical and state-of-the-art unsupervised generic text generation models on three datasets (a synthetic dataset, a descriptive text dataset and a sentimental text dataset). Both qualitative and quantitative experimental results show that our models produce more realistic samples, and DelibGAN-II performs best.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposes using a deliberation network approach to text generation with GANs. It does this by using two decoders where the discriminator is trained on the second decoder and signals from that training are also used to improve the first decoder. Since both approaches have been published before (deliberation networks and policy-gradient based GAN training), the novelty seems to be limited. Additionally, the experiments appear to mostly be focused on single sentence text generation, which is usually not an issue for maximum likelihood trained models. The poor samples from DelibMLE appear much worse than typical maximum likelihood trained language models. Since the model sizes and hyperparameters are not given, it*s possible this is due to an overly small model. The lack of a detailed description of the model hyperparameters would also make this work very hard to reproduce. There*s also very little description of the datasets, for example, the length of a typical example. Finally, while the human evaluation results are interesting, the lack of standard deviations make it hard to measure the significance of the results, especially when only 100 sentences from each model is rated.
assistant
 This paper proposes using a deliberation network approach to text generation with GANs. It does this by using two decoders where the discriminator is trained on the second decoder and signals from that training are also used to improve the first decoder. Since both approaches have been published before (deliberation networks and policy-gradient based GAN training), the novelty seems to be limited. Additionally, the experiments appear to mostly be focused on single sentence text generation, which is usually not an issue for maximum likelihood trained models. The poor samples from DelibMLE appear much worse than typical maximum likelihood trained language models. Since the model sizes and hyperparameters are not given, it*s possible this is due to an overly small model. The lack of a detailed description of the model hyperparameters would also make this work very hard to reproduce. There*s also very little description of the datasets, for example, the length of a typical example. Finally, while the human evaluation results are interesting, the lack of standard deviations make it hard to measure the significance of the results, especially when only 100 sentences from each model is rated.