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].

[EX Q]: The paper shows promising results but it is difficult to read and follow. It presents different things closely related and it is difficult to asses the performance of each one. Diversity, sparsity, regularization term, tying weights. Anyway results are good.
[EX A]: en

[EX Q]: Overall, this is a nice paper. Developing a unifying framework for these newer neural models is a worthwhile endeavor. However, it's unclear if the DRAGNN framework (in its current form) is a significant standalone contribution. The main idea is straightforward: use a transition system to unroll a computation graph. When you implement models in this way you can reuse code because modules can be mixed and matched. This is nice, but (in my opinion) is just good software engineering, not machine learning research. Moreover, there appears to be little incentive to use DRAGNN, as there are no 'free things' (benefits) that you get by using the framework. For example: - If you write your neuralnet in an automatic differentiation library (e.g., tensorflow or dynet) you get gradients for 'free'. - In the VW framework, there are efficiency tricks that 'the credit assignment compiler' provides for you, which would be tedious to implement on your own. There is also a variety of algorithms for training the model in a principled way (i.e., without exposure bias). I don't feel that my question about the limitations of the framework has been satisfactorily addressed. Let me ask it in a different way: Can you give me examples of a few models that I can't (nicely) express in the DRAGNN framework? What if I wanted to implement
[EX A]: en

[EX Q]: The paper presents a medical test based on images of tissue sections. The approach proposed to cancer cells segmentation (membrane localisation) is based on local pixel features and SVM classification. The paper describes quite well the practical problem it deals with (cancer cells segmentation on images) and it is generally well written. - The paper approach described in the paper seems a first try on the problem and follows others in a quite similar (but new) problem. Moreover, the paper concludes that the proposed approach is not good enough. - The features used for classification lacks of frequency information around each pixel (although it used the first derivative with sobel). Most texture descriptors in the literature use frequency information from gabor wavelets, fourier coefficients, DCT, etc. Why not use this kind of information? Why you have chosen this 7 features and not other ones? The paper lacks of experimental evaluation on different features sets.
[EX A]:
en