Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.
The authors propose transfer learning variants for neural-net-based models, applied to a bunch of NLP tagging tasks. The field of multi-tasking is huge, and the approaches proposed here do not seem to be very novel in terms of machine learning: parts of a general architecture for NLP are shared, the amount of shared *layers* being dependent of the task of interest. The novelty lies in the type of architecture which is used in the particular setup of NLP tagging tasks. The experimental results show that the approach seems to work well when there is not much labeled data available (Figure 2). Table 3 show some limited improvement at full scale. Figure 2 results are debatable though: it seems the authors fixed the architecture size while varying the amount of labeled data; it is very likely that tuning the architecture for each size would have led to better results. Overall, while the paper reads well, the novelty seems a bit limited and the experimental section seems a bit disappointing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.
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 propose transfer learning variants for neural-net-based models, applied to a bunch of NLP tagging tasks. The field of multi-tasking is huge, and the approaches proposed here do not seem to be very novel in terms of machine learning: parts of a general architecture for NLP are shared, the amount of shared *layers* being dependent of the task of interest. The novelty lies in the type of architecture which is used in the particular setup of NLP tagging tasks. The experimental results show that the approach seems to work well when there is not much labeled data available (Figure 2). Table 3 show some limited improvement at full scale. Figure 2 results are debatable though: it seems the authors fixed the architecture size while varying the amount of labeled data; it is very likely that tuning the architecture for each size would have led to better results. Overall, while the paper reads well, the novelty seems a bit limited and the experimental section seems a bit disappointing.
assistant
The authors propose transfer learning variants for neural-net-based models, applied to a bunch of NLP tagging tasks. The field of multi-tasking is huge, and the approaches proposed here do not seem to be very novel in terms of machine learning: parts of a general architecture for NLP are shared, the amount of shared *layers* being dependent of the task of interest. The novelty lies in the type of architecture which is used in the particular setup of NLP tagging tasks. The experimental results show that the approach seems to work well when there is not much labeled data available (Figure 2). Table 3 show some limited improvement at full scale. Figure 2 results are debatable though: it seems the authors fixed the architecture size while varying the amount of labeled data; it is very likely that tuning the architecture for each size would have led to better results. Overall, while the paper reads well, the novelty seems a bit limited and the experimental section seems a bit disappointing.