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.
 This paper extends the concept of global rather than local optimization from the learning to search (L2S) literature to RNNs, specifically in the formation and implementation of SEARNN. Their work takes steps to consider and resolve issues that arise from restricting optimization to only local ground truth choices, which traditionally results in label / transition bias from the teacher forced model. The underlying issue (MLE training of RNNs) is well founded and referenced, their introduction and extension to the L2S techniques that may help resolve the issue are promising, and their experiments, both small and large, show the efficacy of their technique. I am also glad to see the exploration of scaling SEARNN to the IWSLT*14 de-en machine translation dataset. As noted by the authors, it is a dataset that has been tackled by related papers and importantly a well scaled dataset. For SEARNN and related techniques to see widespread adoption, the scaling analysis this paper provides is a fundamental component. This reviewer, whilst not having read all of the appendix in detail, also appreciates the additional insights provided by it, such as including losses that were attempted but did not result in appreciable gains. Overall I believe this is a paper that tackles an important topic area and provides a novel and persuasive potential solution to many of the issues it highlights. (extremely minor typo: "One popular possibility from L2S is go the full reduction route down to binary classification")
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>



 This paper extends the concept of global rather than local optimization from the learning to search (L2S) literature to RNNs, specifically in the formation and implementation of SEARNN. Their work takes steps to consider and resolve issues that arise from restricting optimization to only local ground truth choices, which traditionally results in label / transition bias from the teacher forced model. The underlying issue (MLE training of RNNs) is well founded and referenced, their introduction and extension to the L2S techniques that may help resolve the issue are promising, and their experiments, both small and large, show the efficacy of their technique. I am also glad to see the exploration of scaling SEARNN to the IWSLT*14 de-en machine translation dataset. As noted by the authors, it is a dataset that has been tackled by related papers and importantly a well scaled dataset. For SEARNN and related techniques to see widespread adoption, the scaling analysis this paper provides is a fundamental component. This reviewer, whilst not having read all of the appendix in detail, also appreciates the additional insights provided by it, such as including losses that were attempted but did not result in appreciable gains. Overall I believe this is a paper that tackles an important topic area and provides a novel and persuasive potential solution to many of the issues it highlights. (extremely minor typo: "One popular possibility from L2S is go the full reduction route down to binary classification")
assistant
 This paper extends the concept of global rather than local optimization from the learning to search (L2S) literature to RNNs, specifically in the formation and implementation of SEARNN. Their work takes steps to consider and resolve issues that arise from restricting optimization to only local ground truth choices, which traditionally results in label / transition bias from the teacher forced model. The underlying issue (MLE training of RNNs) is well founded and referenced, their introduction and extension to the L2S techniques that may help resolve the issue are promising, and their experiments, both small and large, show the efficacy of their technique. I am also glad to see the exploration of scaling SEARNN to the IWSLT*14 de-en machine translation dataset. As noted by the authors, it is a dataset that has been tackled by related papers and importantly a well scaled dataset. For SEARNN and related techniques to see widespread adoption, the scaling analysis this paper provides is a fundamental component. This reviewer, whilst not having read all of the appendix in detail, also appreciates the additional insights provided by it, such as including losses that were attempted but did not result in appreciable gains. Overall I believe this is a paper that tackles an important topic area and provides a novel and persuasive potential solution to many of the issues it highlights. (extremely minor typo: "One popular possibility from L2S is go the full reduction route down to binary classification")