Published as a conference paper at ICLR 2018 DEEP ACTIVE LEARNING FOR NAMED ENTITY RECOGNITION
Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25% of the original training data.
This paper studies the application of different existing active learning strategies for the deep models for NER.
Pros:
* Active learning may be used for improving the performance of deep models for NER in practice
* All the proposed approaches are sound and the experimental results showed that active learning is beneficial for the deep models for NER
Cons:
* The novelty of this paper is marginal. The proposed approaches turn out to be a combination of existing active learning strategies for selecting data to query with the existing deep model for NER. 
* No conclusion can be drawn by comparing with the 4 different strategies.
======= After rebuttal  ================
Thank you for the clarification and revision on this paper. It looks better now.
I understand that the purpose of this paper is to give actionable insights to the practice of deep learning. However, since AL itself is a meta learning framework and neural net as the base learner has been shown to be effective for AL, the novelty and contribution of a general discussion of applying AL for deep neural nets is marginal.  What I really expected is a tightly-coupled active learning strategy that is specially designed for the particular deep neural network structure used for NER. Apparently, however, none of the strategies used in this work is designed for this purpose (e.g., the query strategy or model update strategy should at least reflex some properties of deep learning or NER). Thus, it is still below my expectation. 
Anyway, since the authors had attempted to improved this paper, and the results may provide some information to practice, I would like to slightly raise my rating to give this attempt a chance.