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].

This paper is technically sound. It highlights well the strengths and weaknesses of the proposed simplified model.  In terms of impact, its novelty is limited, in the sense that the authors did seemingly the right thing and obtained the expected outcomes. The idea of modeling deep learning computation is not in itself particularly novel. As a companion paper to an open source release of the model, it would meet my bar of acceptance in the same vein as a paper describing a novel dataset, which might not provide groundbreaking insights, yet be generally useful to the community.  In the absence of released code, even if the authors promise to release it soon, I am more ambivalent, since that's where all the value lies. It would also be a different story if the authors had been able to use this framework to make novel architectural decisions that improved training scalability in some way, and incorporated such new insights in the paper.  UPDATED: code is now available. Revised review accordingly.
en

- Strengths:  This is the first neural network-based approach to argumentation mining. The proposed method used a Pointer Network (PN) model with multi-task learning and outperformed previous methods in the experiments on two datasets.  - Weaknesses:  This is basically an application of PN to argumentation mining. Although the combination of PN and multi-task learning for this task is novel, its novelty is not enough for ACL long publication. The lack of qualitative analysis and error analysis is also a major concern.  - General Discussion:  Besides the weaknesses mentioned above, the use of PN is not well-motivated. Although three characteristics of PN were described in l.138-143, these are not a strong motivation against the use of bi-directional LSTMs and the attention mechanism. The authors should describe what problems are solved by PN and discuss in the experiments how much these problems are solved.  Figures 2 and 3 are difficult to understand. What are the self link to D1 and the links from D2 to E1 and D3/D4 to E2? These are just the outputs from the decoder and not links. The decoder LSTM does not have an input from e_j in these figures, but it does in Equation (3). Also, in Figure 3, the abbreviation "FC" is not defined.  Equation (8) is strange. To calculate the probability of each component type, the probability of E_i is calculated.  In the experiments, I did not understand why only "PN", which is not a joint model, was performed for the microtext corpus.  It is not clear whether the BLSTM model is trained with the joint-task objective.  There are some studies on discourse parsing using the attention mechanism. The authors should describe the differences from these studies.  Minor issues:  l.128: should related -> should be related  l.215: (2015) is floating  l.706: it able -> it is able  I raised my recommendation score after reading the convincing author responses. I strongly recommend that the authors should discuss improved examples by PN as well as the details of feature ablation.
en

This paper describes an approach to learning the non-linear activation function in deep neural nets.  This is achieved by representing the activation function in a basis of non-linear functions and learning the coefficients.  Authors use Fourier basis in the paper.  A theoretical analysis of the proposed approach is also presented, using algorithmic stability arguments, to demonstrate good generalization behavior (vanishing generalization error with large data sets) of networks with learnt non-linearities.  The main question I have about this paper is that writing a non-linear activation function as a linear or affine combination of other non-linear basis functions is equivalent to making a larger network whose nodes have the basis functions as non-linearities and whose weights have certain constraints on them.  Thus, the value of the proposed approach of learning non-linearities over optimizing network capacity for a given task (with fixed non-linearities) is not clear to me.  Or could it be argued that the constrained implied by learnt non-linearity approach are somehow good thing to do?  Another question - In the two stage training process for CNNs, when ReLU activation is replaced by NPFC(L,T), is the NPFC(L,T) activation initialized to approximate ReLU, or is it initialized using random coefficients?  Few minor corrections/questions: - Pg 2. “ … the interval [-L+T, L+T] …” should be “ … the interval [-L+T, L-T] … “ ? - Pg 2., Equation for f(x), should it be “ (-L+T) i \pi x / L “ in both sin and cos terms, or without “ x “ ? - Theorem 4.2 “ … some algorithm \eps-uniformly stable …” remove the word “algorithm” - Theorem 4.5.  SGM undefined

en