We propose a model to tackle classification tasks in the presence of very little training data. To this aim, we introduce a novel matching mechanism to focus on elements of the input, by using vectors that represent semantically meaningful concepts for the task at hand. By leveraging highlighted portions of the training data, a simple, yet effective, error boosting technique guides the learning process. In practice, it increases the error associated to relevant parts of the input by a given factor. Results on text classification tasks confirm the benefits of the proposed approach in both balanced and unbalanced cases, thus being of practical use when labeling new examples is expensive. In addition, the model is interpretable, as it allows for human inspection of the learned weights.
After responses:
I read the authors response and decided to stick to my original score mostly because:
1 - I understand that interpretability is hard to define. I also agree with the authors response. However, this is still not reflected in the paper in any way. I expect a discussion on what is the relevant definition used in the paper and how does it fit to that definition. Currently, it is very confusing to the reader.
2 - I understand the authors' response that few-shot learning is a different empirical setting. However, authors also agree that settings are some-what relevant. I really do not see any gain by NOT discussing the few-shot learning literature. At the end, a reader is interested in this work if they have limited data. Moreover, other ways to address limited data issue should be discussed.
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The manuscript is proposing a few-shot classification setting in which training set includes only few examples. The main contribution is using prototype embeddings and representing each word as cosine distances to these prototype embeddings. Moreover, the final classification is weighted summation of the per-token decisions followed by a soft-max. Per-token classifiers are obtained with an MLP using the cosine distances as features. When the relevance labels are available, they are used in training to boost gradients.
PRO(s)
The proposed method is interesting and addressing an important problem. There are many few-shot scenarios and finding good models for them is impactful.
The results are promising and the proposed method is more interpretable than the existing NLP classifiers. I disagree with the claim that the model is interpretable. However, I appreciate the effort to interpret the model.
CON(s)
The model is not interpretable because 1) it starts with embeddings and they are not interpretable, 2) model is full of non-linearities and decision boundaries are not possible to find. In other words, it is not possible to answer "what would make this model predict some other classifier".
The authors should discuss the existing few-shot learning mechanisms. Especially, "Prototypical Networks for Few-shot Learning" is very relevant. I also think it can be included as a baseline with very minimal modifications.
The writing is not complete. The authors do not even discuss how the prototypes are learned. I am assuming it is done using full gradient-descent over all parameters. However, this is not clearly discussed. Implementation details should be discussed more clearly.
SUMMARY
I believe the manuscript is definitely interesting and has a potential. In the mean time, It is not ready for publication. It needs a through review of few-shot learning. Authors should also discuss can any of the few-shot learning methods be included in the experimental study. If the answer is yes, it should be. If the answer is no, it should be explained clearly. 
Although my recommendation is weak-reject, I am happy to bump it up if these issues are addressed.