Toward learning better metrics for sequence generation training with policy gradient
Designing a metric manually for unsupervised sequence generation tasks, such as text generation, is essentially difficult. In a such situation, learning a metric of a sequence from data is one possible solution. The previous study, SeqGAN, proposed the framework for unsupervised sequence generation, in which a metric is learned from data, and a generator is optimized with regard to the learned metric with policy gradient, inspired by generative adversarial nets (GANs) and reinforcement learning. In this paper, we make two proposals to learn better metric than SeqGAN*s: partial reward function and expert-based reward function training. The partial reward function is a reward function for a partial sequence of a certain length. SeqGAN employs a reward function for completed sequence only. By combining long-scale and short-scale partial reward functions, we expect a learned metric to be able to evaluate a partial correctness as well as a coherence of a sequence, as a whole. In expert-based reward function training, a reward function is trained to discriminate between an expert (or true) sequence and a fake sequence that is produced by editing an expert sequence. Expert-based reward function training is not a kind of GAN frameworks. This makes the optimization of the generator easier. We examine the effect of the partial reward function and expert-based reward function training on synthetic data and real text data, and show improvements over SeqGAN and the model trained with MLE. Specifically, whereas SeqGAN gains 0.42 improvement of NLL over MLE on synthetic data, our best model gains 3.02 improvement, and whereas SeqGAN gains 0.029 improvement of BLEU over MLE, our best model gains 0.250 improvement.
 This paper describes an approach to generating time sequences by learning state-action values, where the state is the sequence generated so far, and the action is the choice of the next value. Local and global reward functions are learned from existing data sequences and then the Q-function learned from a policy gradient. Unfortunately, this description is a little vague, because the paper*s details are quite difficult to understand. Though the approach is interesting, and the experiments are promising, important explanation is missing or muddled. Perhaps most confusing is the loss function in equation 7, which is quite inadequately explained. This paper could be interesting, but substantial editing is needed before it is sufficient for publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Toward learning better metrics for sequence generation training with policy gradient
Designing a metric manually for unsupervised sequence generation tasks, such as text generation, is essentially difficult. In a such situation, learning a metric of a sequence from data is one possible solution. The previous study, SeqGAN, proposed the framework for unsupervised sequence generation, in which a metric is learned from data, and a generator is optimized with regard to the learned metric with policy gradient, inspired by generative adversarial nets (GANs) and reinforcement learning. In this paper, we make two proposals to learn better metric than SeqGAN*s: partial reward function and expert-based reward function training. The partial reward function is a reward function for a partial sequence of a certain length. SeqGAN employs a reward function for completed sequence only. By combining long-scale and short-scale partial reward functions, we expect a learned metric to be able to evaluate a partial correctness as well as a coherence of a sequence, as a whole. In expert-based reward function training, a reward function is trained to discriminate between an expert (or true) sequence and a fake sequence that is produced by editing an expert sequence. Expert-based reward function training is not a kind of GAN frameworks. This makes the optimization of the generator easier. We examine the effect of the partial reward function and expert-based reward function training on synthetic data and real text data, and show improvements over SeqGAN and the model trained with MLE. Specifically, whereas SeqGAN gains 0.42 improvement of NLL over MLE on synthetic data, our best model gains 3.02 improvement, and whereas SeqGAN gains 0.029 improvement of BLEU over MLE, our best model gains 0.250 improvement.
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 describes an approach to generating time sequences by learning state-action values, where the state is the sequence generated so far, and the action is the choice of the next value. Local and global reward functions are learned from existing data sequences and then the Q-function learned from a policy gradient. Unfortunately, this description is a little vague, because the paper*s details are quite difficult to understand. Though the approach is interesting, and the experiments are promising, important explanation is missing or muddled. Perhaps most confusing is the loss function in equation 7, which is quite inadequately explained. This paper could be interesting, but substantial editing is needed before it is sufficient for publication.
assistant
 This paper describes an approach to generating time sequences by learning state-action values, where the state is the sequence generated so far, and the action is the choice of the next value. Local and global reward functions are learned from existing data sequences and then the Q-function learned from a policy gradient. Unfortunately, this description is a little vague, because the paper*s details are quite difficult to understand. Though the approach is interesting, and the experiments are promising, important explanation is missing or muddled. Perhaps most confusing is the loss function in equation 7, which is quite inadequately explained. This paper could be interesting, but substantial editing is needed before it is sufficient for publication.