SEARNN: Training RNNs with global-local losses
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction. Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes. This allows us to validate the benefits of our approach on a machine translation task.
 The paper proposes new RNN training method based on the SEARN learning to search (L2S) algorithm and named as SeaRnn. It proposes a way of overcoming the limitation of local optimization trough the exploitation of the structured losses by L2S. It can consider different classifiers and loss functions, and a sampling strategy for making the optimization problem scalable is proposed. SeaRnn improves the results obtained by MLE training in three different problems, including a large-vocabulary machine translation. In summary, a very nice paper. Quality: SeaRnn is a well rooted and successful application of the L2S strategy to the RNN training that combines at the same time global optimization and scalable complexity. Clarity: The paper is well structured and written, with a nice and well-founded literature review. Originality: the paper presents a new algorithm for training RNN based on the L2S methodology, and it has been proven to be competitive in both toy and real-world problems. Significance: although the application of L2S to RNN training is not new, the contribution to the overcoming the limitations due to error propagation and MLE training of RNN is substantial.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SEARNN: Training RNNs with global-local losses
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction. Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes. This allows us to validate the benefits of our approach on a machine translation task.
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 paper proposes new RNN training method based on the SEARN learning to search (L2S) algorithm and named as SeaRnn. It proposes a way of overcoming the limitation of local optimization trough the exploitation of the structured losses by L2S. It can consider different classifiers and loss functions, and a sampling strategy for making the optimization problem scalable is proposed. SeaRnn improves the results obtained by MLE training in three different problems, including a large-vocabulary machine translation. In summary, a very nice paper. Quality: SeaRnn is a well rooted and successful application of the L2S strategy to the RNN training that combines at the same time global optimization and scalable complexity. Clarity: The paper is well structured and written, with a nice and well-founded literature review. Originality: the paper presents a new algorithm for training RNN based on the L2S methodology, and it has been proven to be competitive in both toy and real-world problems. Significance: although the application of L2S to RNN training is not new, the contribution to the overcoming the limitations due to error propagation and MLE training of RNN is substantial.
assistant
 The paper proposes new RNN training method based on the SEARN learning to search (L2S) algorithm and named as SeaRnn. It proposes a way of overcoming the limitation of local optimization trough the exploitation of the structured losses by L2S. It can consider different classifiers and loss functions, and a sampling strategy for making the optimization problem scalable is proposed. SeaRnn improves the results obtained by MLE training in three different problems, including a large-vocabulary machine translation. In summary, a very nice paper. Quality: SeaRnn is a well rooted and successful application of the L2S strategy to the RNN training that combines at the same time global optimization and scalable complexity. Clarity: The paper is well structured and written, with a nice and well-founded literature review. Originality: the paper presents a new algorithm for training RNN based on the L2S methodology, and it has been proven to be competitive in both toy and real-world problems. Significance: although the application of L2S to RNN training is not new, the contribution to the overcoming the limitations due to error propagation and MLE training of RNN is substantial.