Paper:
Workshop track -ICLR 2018 FAST AND ACCURATE TEXT CLASSIFICATION: SKIM- MING, REREADING AND EARLY STOPPING
Recent advances in recurrent neural nets (RNNs) have shown much promise in many applications in natural language processing. For most of these tasks, such as sentiment analysis of customer reviews, a recurrent neural net model parses the entire review before forming a decision. We argue that reading the entire input is not always necessary in practice, since a lot of reviews are often easy to classify, i.e., a decision can be formed after reading some crucial sentences or words in the provided text. In this paper, we present an approach of fast reading for text classification. Inspired by several well-known human reading techniques, our approach implements an intelligent recurrent agent which evaluates the importance of the current snippet in order to decide whether to make a prediction, or to skip some texts, or to re-read part of the sentence. Our agent uses an RNN module to encode information from the past and the current tokens, and applies a policy module to form decisions. With an end-to-end training algorithm based on policy gradient, we train and test our agent on several text classification datasets and achieve both higher efficiency and better accuracy compared to previous approaches.

Review:
This paper proposes to augment RNNs for text classification with a mechanism that decides whether the RNN should re-read a token, skip a number of tokens, or stop and output the prediction. The motivation is that one can stop reading before the end of the text and/or skip some words and still arrive to the same answer but faster.
The idea is intriguing, even though not entirely novel. Apart from the Yu et al. (2017) cited, there is older work trying to save computational time in NLP, e.g.: 
Dynamic Feature Selection for Dependency Parsing.
He He, Hal Daumé III and Jason Eisner.
Empirical Methods in Natural Language Processing (EMNLP), 2013
that decides whether to extract a feature or not.
However, what is not clear to me what is achieved here. In the example shown in Figure 5 it seems like what happens is that by not reading the whole text the model avoids passages that might be confusing it. This could improve predictive accuracy (as it seems to do), as long as the model can handle better the earlier part of the text. But this is just an assumption, which is not guaranteed in any way. It could be that the earlier parts of the text are hard for the model. In a way, it feels more like we are addressing a limitation of RNN models in understanding text. 
Pros:
- The idea is intersting and if evaluated thoroughly it could be quite influential.
Cons:
- the introduction states that there are two kinds of NLP problems, sequence2sequence and sequence2scalar. I found this rather confusing since text classification falls in the latter presumably, but the output is a label. Similarly, PoS tagging has a linear chain as its output, can't see why it is sequence2scalar. I think there is a confusion between the methods used for a task, and the task itself. Being able to apply a sequence-based model to a task, doesn't make it sequential necessarily.
- the comparison in terms of FLOPs is a good idea. But wouldn't the relative savings depend on the architecture used for the RNN and the RL agent? E.g. it could be that the RL network is more complicated and using it costs more than what it saves in the RNN operations.
- While table 2 reports the savings vs the full reading model, we don't know how much worse the model got for these savings. 
- Having a trade-off between savings and accuracy is a good idea too. I would have liked to see an experiment showing how many FLOPs we can save for the same performance, which should be achievable by adjusting the alpha parameter.
- The experiments are conducted on previously published datasets. It would be good to have some previously published results on them to get a sense of how good the RNN model used is.
- Why not use smaller chunks? 20 words or one sentence at the time is rather coarse. If anything, it should help the model proposed achieve greater savings. How much does the choice of chunk matter?
- it is stated in the conclusion that the advantage actor-critic used is beneficial, however no experimental comparison is shown. Was it used for the Yu et al. baseline too?
- It is stated that model hardly relies on any hyperparameters; in comparison to what? It is better to quantify such statements,