Generating Text through Adversarial Training using Skip-Thought Vectors
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.
 This paper presents an approach of using GANs for text generation by training a Skip-thought sentence embedding model and applying GANs to distinguish embeddings from the training corpus encoded with a skip thought encoder and sentence embeddings generated by a CNN. While the paper gives some initial experimental results in table 1, there are major gaps in the experiments. For example, there are no comparisons with maximum-likelihood trained models or comparisons with other existing GAN architectures. Without these comparisons, it*s hard to see the benefit of having the skip-thought architecture compared to the existing text GAN literature such as SeqGAN and MaskGAN. The improvements gained with Wasserstein and gradient penalties are interesting but results on more datasets and longer samples are needed, especially section 4.2 would benefit from quantitative and human evaluation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generating Text through Adversarial Training using Skip-Thought Vectors
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.
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 presents an approach of using GANs for text generation by training a Skip-thought sentence embedding model and applying GANs to distinguish embeddings from the training corpus encoded with a skip thought encoder and sentence embeddings generated by a CNN. While the paper gives some initial experimental results in table 1, there are major gaps in the experiments. For example, there are no comparisons with maximum-likelihood trained models or comparisons with other existing GAN architectures. Without these comparisons, it*s hard to see the benefit of having the skip-thought architecture compared to the existing text GAN literature such as SeqGAN and MaskGAN. The improvements gained with Wasserstein and gradient penalties are interesting but results on more datasets and longer samples are needed, especially section 4.2 would benefit from quantitative and human evaluation.
assistant
 This paper presents an approach of using GANs for text generation by training a Skip-thought sentence embedding model and applying GANs to distinguish embeddings from the training corpus encoded with a skip thought encoder and sentence embeddings generated by a CNN. While the paper gives some initial experimental results in table 1, there are major gaps in the experiments. For example, there are no comparisons with maximum-likelihood trained models or comparisons with other existing GAN architectures. Without these comparisons, it*s hard to see the benefit of having the skip-thought architecture compared to the existing text GAN literature such as SeqGAN and MaskGAN. The improvements gained with Wasserstein and gradient penalties are interesting but results on more datasets and longer samples are needed, especially section 4.2 would benefit from quantitative and human evaluation.