text: This work proposes an approach for transcription factor binding site prediction using a multi-label classification formulation.  It is a very interesting problem  and application and the approach is interesting.  Novelty: The method is quite similar to matching networks (Vinyals, 2016) with a few changes in the matching approach.  As such, in order to establish its broader applicability there should be additional evaluation on other benchmark datasets.  The MNIST performance comparison is inadequate  and there are other papers that do better on it.  They should clearly list what the contributions are w.r.t to the work by Vinyals et al 2016. They should also cite works that learn embeddings in a multi-label setting such as StarSpace. Impact: In its current form the paper seems to be most relevant to the computational biology / TFBS community.  However, there is no comparison to the exact networks used in the prior works DeepBind/DeepSea/DanQ/Basset/DeepLift or bidirectional LSTMs.  Further there is no comparison to existing one-shot learning techniques either.  This greatly limits the impact of the work. For biological impact, a comparison to any of the motif learning approaches that are popular in the biology/comp-bio community will help (for instance, HOMER, FIMO). Cons: The authors claim they can learn TF-TF interactions and it is one of the main biological contributions,  but there is no evidence of why  (beyond very preliminary evaluation using the Trrust database).  Their examples are 200-bp long which does not mean that all TFs binding in that window are involved in cooperative binding.  The prototype loss is too simplistic to capture co-binding tendencies  and the combinationLSTM is not well motivated.  One interesting source of information they could tap into for TF-TF interactions is CAP-SELEX (Jolma et al, Nature 2015). One of the main drawbacks is the lack of interpretability of their model where approaches like DanQ/DeepLift etc benefit.  The PWM-like filters in some of the prior works help understand what type of sequence properties contribute to binding events.  Can their model lead to an understanding of this sort? Evaluation: The empirical evaluation itself is not very strong  as there are only modest improvements over simple baselines.  Further there are no error-bars etc to indicate the variance in their performance numbers. It will be useful to have a TF-level performance split-up to get an idea of which TFs benefit most. Clarity: The paper can benefit from more clarity in the technical aspects.  It is hard to follow for anyone not already familiar with matching networks.  The objective function, parameters need to be clearly introduced in one place.  For instance, what is y_i in their multi-label framework? Various choices are not well motivated; for instance cosine similarity, the value of hyperparameter epsilon. The prototype vectors are not motif-like at all --  can the authors motivate this aspect better?