In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

Approaches like adaptive dropout also have the binary mask as a function of input to a neuron very similar to the proposed approach. It is not clear, even from the new draft, how the proposed approach differs to Adaptive dropout in terms of functionality. The experimental validation is also not extensive since comparison to SOTA is not included.
en

The paper proposed to analyze several recently developed machine readers and found that some machine readers could potentially take advantages of the entity marker (given that the same marker points out to the same entity). I usually like analysis papers, but I found the argument proposed in this paper not very clear.  I like the experiments on the Stanford reader, which shows that the entity marker in fact helps the Stanford reader on WDW. I found that results rather interesting.  However, I found the organization and the overall message of this paper quite confusing. First of all, it feels that the authors want to explain the above behavior with some definition of the “structures”. However, I am not sure that how successful the attempt is. For me, it is still not clear what the structures are. This makes reading section 4 a bit frustrating.   I am also not sure what is the take home message of this paper. Does it mean that the entity marking should be used in the MR models? Should we design models that can also model the entity reference at the same time? What are the roles of the linguistic features here? Should we use linguistic structure to overcome the reference issue?  Overall, I feel that the analysis is interesting, but I feel that the paper can benefit from having a more focused argument.
en

The idea of combining many modalities for product recommendation is a good one and well worth exploring. However, the approach presented in this paper is unsatisfying, as it involves combining several pre-trained models, in a somewhat ad hoc manner. Overall a nice problem, but the formulation and results are not presented clearly enough.

en