Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction. While traditional seq2seq models are trained by Maximum Likelihood Estimation (MLE), much recent work has made various attempts to optimize evaluation scores directly to solve the mismatch between training and evaluation, since model predictions are usually evaluated by a task specific evaluation metric like BLEU or ROUGE scores instead of perplexity. This paper puts this existing work into two categories, a) minimum divergence, and b) maximum margin. We introduce a new training criterion based on the analysis of existing work, and empirically compare models in the two categories. Our experimental results show that our new training criterion can usually work better than existing methods, on both the tasks of machine translation and sentence summarization.
 This paper compares several well known methods and illustrates some connections among these methods, and proposes one new method based on Hellinger distance for seq2seq models* training, experimental results on machine translation and sentence summarization show that the method based on Hellinger distance gives the best results. However the originality and significance of this work are weak.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction. While traditional seq2seq models are trained by Maximum Likelihood Estimation (MLE), much recent work has made various attempts to optimize evaluation scores directly to solve the mismatch between training and evaluation, since model predictions are usually evaluated by a task specific evaluation metric like BLEU or ROUGE scores instead of perplexity. This paper puts this existing work into two categories, a) minimum divergence, and b) maximum margin. We introduce a new training criterion based on the analysis of existing work, and empirically compare models in the two categories. Our experimental results show that our new training criterion can usually work better than existing methods, on both the tasks of machine translation and sentence summarization.
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 compares several well known methods and illustrates some connections among these methods, and proposes one new method based on Hellinger distance for seq2seq models* training, experimental results on machine translation and sentence summarization show that the method based on Hellinger distance gives the best results. However the originality and significance of this work are weak.
assistant
 This paper compares several well known methods and illustrates some connections among these methods, and proposes one new method based on Hellinger distance for seq2seq models* training, experimental results on machine translation and sentence summarization show that the method based on Hellinger distance gives the best results. However the originality and significance of this work are weak.