Language GANs Falling Short
Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, the model instead reads its previously generated samples - a phenomenon coined exposure bias. Exposure bias was hypothesized to be a root cause of poor sample quality and thus many generative adversarial networks (GANs) were proposed as a remedy since they have identical training and inference. However, many of the ensuing GAN variants validated sample quality improvements but ignored loss of sample diversity. This work reiterates the fallacy of quality-only metrics and clearly demonstrate that the well-established technique of reducing softmax temperature can outperform GANs on a quality-only metric. Further, we establish a definitive quality-diversity evaluation procedure using temperature tuning over local and global sample metrics. Under this, we find that MLE models consistently outperform the proposed GAN variants over the whole quality-diversity space. Specifically, we find that 1) exposure bias appears to be less of an issue than the complications arising from non-differentiable, sequential GAN training; 2) MLE trained models provide a better quality/diversity trade-off compared to their GAN counterparts, all while being easier to train, easier to cross-validate, and less computationally expensive.
Recently many language GAN papers have been published to overcome the so called exposure bias, and demonstrated improvements in natural language generation in terms of sample quality, some works propose to assess the generation in terms of diversity, however, quality and diversity are two conflicting measures that are hard to meet. This paper is a groundbreaking work that proposes receiver operating curve or Pareto optimality for quality and diversity measures, and shows that simple temperature sweeping in MLE generates the best quality-diversity curves than all language GAN models through comprehensive experiments. It points out a good target that language GANs should aims at. For the experiments on long-text generation using EMNLP news 2017, it is not clear how the data is partitioned as training data, validation data and test data to get the results in Figures 4(a), moreover for the results for LeakGAN, RankGAN SeqGAN, MaliGAN, it seems that they are copied from other papers, but again how the data set is partitioned is not clear for example in SeqGAN*s paper, and most likely, the data is partitioned in a different way, so the results are not comparable. The authors should run the code and get the results on it own. An important RL-free language GAN paper is missing, Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao: Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators. AISTATS 2019: 3089-3098. This paper directly and adversarially train a language model without MLE pre-training and obtains good results, it is better to compare the results. Typo: page 1, the second line from bottom, as a computationally, not an
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Language GANs Falling Short
Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, the model instead reads its previously generated samples - a phenomenon coined exposure bias. Exposure bias was hypothesized to be a root cause of poor sample quality and thus many generative adversarial networks (GANs) were proposed as a remedy since they have identical training and inference. However, many of the ensuing GAN variants validated sample quality improvements but ignored loss of sample diversity. This work reiterates the fallacy of quality-only metrics and clearly demonstrate that the well-established technique of reducing softmax temperature can outperform GANs on a quality-only metric. Further, we establish a definitive quality-diversity evaluation procedure using temperature tuning over local and global sample metrics. Under this, we find that MLE models consistently outperform the proposed GAN variants over the whole quality-diversity space. Specifically, we find that 1) exposure bias appears to be less of an issue than the complications arising from non-differentiable, sequential GAN training; 2) MLE trained models provide a better quality/diversity trade-off compared to their GAN counterparts, all while being easier to train, easier to cross-validate, and less computationally expensive.
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>



Recently many language GAN papers have been published to overcome the so called exposure bias, and demonstrated improvements in natural language generation in terms of sample quality, some works propose to assess the generation in terms of diversity, however, quality and diversity are two conflicting measures that are hard to meet. This paper is a groundbreaking work that proposes receiver operating curve or Pareto optimality for quality and diversity measures, and shows that simple temperature sweeping in MLE generates the best quality-diversity curves than all language GAN models through comprehensive experiments. It points out a good target that language GANs should aims at. For the experiments on long-text generation using EMNLP news 2017, it is not clear how the data is partitioned as training data, validation data and test data to get the results in Figures 4(a), moreover for the results for LeakGAN, RankGAN SeqGAN, MaliGAN, it seems that they are copied from other papers, but again how the data set is partitioned is not clear for example in SeqGAN*s paper, and most likely, the data is partitioned in a different way, so the results are not comparable. The authors should run the code and get the results on it own. An important RL-free language GAN paper is missing, Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao: Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators. AISTATS 2019: 3089-3098. This paper directly and adversarially train a language model without MLE pre-training and obtains good results, it is better to compare the results. Typo: page 1, the second line from bottom, as a computationally, not an
assistant
Recently many language GAN papers have been published to overcome the so called exposure bias, and demonstrated improvements in natural language generation in terms of sample quality, some works propose to assess the generation in terms of diversity, however, quality and diversity are two conflicting measures that are hard to meet. This paper is a groundbreaking work that proposes receiver operating curve or Pareto optimality for quality and diversity measures, and shows that simple temperature sweeping in MLE generates the best quality-diversity curves than all language GAN models through comprehensive experiments. It points out a good target that language GANs should aims at. For the experiments on long-text generation using EMNLP news 2017, it is not clear how the data is partitioned as training data, validation data and test data to get the results in Figures 4(a), moreover for the results for LeakGAN, RankGAN SeqGAN, MaliGAN, it seems that they are copied from other papers, but again how the data set is partitioned is not clear for example in SeqGAN*s paper, and most likely, the data is partitioned in a different way, so the results are not comparable. The authors should run the code and get the results on it own. An important RL-free language GAN paper is missing, Zhongliang Li, Tian Xia, Xingyu Lou, Kaihe Xu, Shaojun Wang, Jing Xiao: Adversarial Discrete Sequence Generation without Explicit NeuralNetworks as Discriminators. AISTATS 2019: 3089-3098. This paper directly and adversarially train a language model without MLE pre-training and obtains good results, it is better to compare the results. Typo: page 1, the second line from bottom, as a computationally, not an