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.
This paper presents a clear hierarchical taxonomy of transfer learning methods as applicable to sequence tagging problems. This contextualizes and unifies previous work on specific instances of this taxonomy. Moreover, the paper shows that previously unexplored places in this taxonomy are competitive with or superior to the state of the art in key benchmark problems. It*d be nice to see this explored further, such as highlighting what is the loss as you move from the more restrictive to the less restrictive transfer learning approaches, but I believe this paper is interesting and acceptable as-is.
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>



This paper presents a clear hierarchical taxonomy of transfer learning methods as applicable to sequence tagging problems. This contextualizes and unifies previous work on specific instances of this taxonomy. Moreover, the paper shows that previously unexplored places in this taxonomy are competitive with or superior to the state of the art in key benchmark problems. It*d be nice to see this explored further, such as highlighting what is the loss as you move from the more restrictive to the less restrictive transfer learning approaches, but I believe this paper is interesting and acceptable as-is.
assistant
This paper presents a clear hierarchical taxonomy of transfer learning methods as applicable to sequence tagging problems. This contextualizes and unifies previous work on specific instances of this taxonomy. Moreover, the paper shows that previously unexplored places in this taxonomy are competitive with or superior to the state of the art in key benchmark problems. It*d be nice to see this explored further, such as highlighting what is the loss as you move from the more restrictive to the less restrictive transfer learning approaches, but I believe this paper is interesting and acceptable as-is.