Connecting the Dots Between MLE and RL for Sequence Generation
Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms. For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem. Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency. A variety of other algorithms such as RAML, SPG, and data noising, have also been developed in different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters. The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency. Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning. Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.
 The authors provide a common mathematical perspective on several learning algorithms for sequence models. They also introduce a new algorithm that combines several of the existing ones and achieves significant (but small) improvements on a machine translation and a text summarization task. The paper is clearly written, giving a good exposition of the unifying formulation. I believe the paper is quite insightful, and contributes to the community*s understanding of the learning algorithms. However, the improvements in their experiments are rather small, and could probably be improved with more experimental work. They do showcase the usefulness of their new formulation, but not very strongly. Thus my recommendation to accept the paper is mostly based on the theoretical content that opens an interesting new perspective.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Connecting the Dots Between MLE and RL for Sequence Generation
Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms. For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem. Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency. A variety of other algorithms such as RAML, SPG, and data noising, have also been developed in different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters. The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency. Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning. Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.
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>



 The authors provide a common mathematical perspective on several learning algorithms for sequence models. They also introduce a new algorithm that combines several of the existing ones and achieves significant (but small) improvements on a machine translation and a text summarization task. The paper is clearly written, giving a good exposition of the unifying formulation. I believe the paper is quite insightful, and contributes to the community*s understanding of the learning algorithms. However, the improvements in their experiments are rather small, and could probably be improved with more experimental work. They do showcase the usefulness of their new formulation, but not very strongly. Thus my recommendation to accept the paper is mostly based on the theoretical content that opens an interesting new perspective.
assistant
 The authors provide a common mathematical perspective on several learning algorithms for sequence models. They also introduce a new algorithm that combines several of the existing ones and achieves significant (but small) improvements on a machine translation and a text summarization task. The paper is clearly written, giving a good exposition of the unifying formulation. I believe the paper is quite insightful, and contributes to the community*s understanding of the learning algorithms. However, the improvements in their experiments are rather small, and could probably be improved with more experimental work. They do showcase the usefulness of their new formulation, but not very strongly. Thus my recommendation to accept the paper is mostly based on the theoretical content that opens an interesting new perspective.