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]: This work address the problem of supervised learning from strongly labeled data with label noise. This is a very practical and relevant problem in applied machine learning. The authors note that using sampling approaches such as EM isn't effective, too slow and cannot be integrated into end-to-end training. Thus, they propose to simulate the effects of EM by a noisy adaptation layer, effectively a softmax, that is added to the architecture during training, and is omitted at inference time. The proposed algorithm is evaluated on MNIST and shows improvements over existing approaches that deal with noisy labeled data. A few comments. 1. There is no discussion in the work about the increased complexity of training for the model with two softmaxes. 2. What is the rationale for having consecutive (serialized) softmaxes, instead of having a compound objective with two losses, or a network with parallel losses and two sets of gradients? 3. The proposed architecture with only two hidden layers isn't not representative of larger and deeper models that are practically used, and it is not clear that shown results will scale to bigger networks. 4. Why is the approach only evaluated on MNIST, a dataset that is unrealistically simple.
[EX A]: en

[EX Q]: The paper describes an idea to learn phrasal representation and facilitate them in RNN-based language models and neural machine translation -Strengths: The idea to incorporate phrasal information into the task is interesting. - Weaknesses: - The description is hard to follow. Proof-reading by an English native speaker would benefit the understanding - The evaluation of the approach has several weaknesses - General discussion - In Equation 1 and 2 the authors mention a phrase representation give a fix-length word embedding vector. But this is not used in the model. The representation is generated based on an RNN. What the propose of this description? - Why are you using GRU for the Pyramid and LSTM for the sequential part? Is the combination of two architectures a reason for your improvements? - What is the simplified version of the GRU? Why is it performing better? How is it performing on the large data set? - What is the difference between RNNsearch (groundhog) and RNNsearch(baseline) in Table 4? - What is the motivation for only using the ending phrases and e.g. not using the starting phrases? - Did you use only the pyramid encoder? How is it performing? That would be a more fair comparison since it normally helps to make the model more complex. - Why did you run RNNsearch several times, but PBNMT only once? - Section 5.2: What is the intent of this section
[EX A]: en

[EX Q]: Overall I think this is an interesting paper which shows empirical performance improvement over baselines. However, my main concern with the paper is regarding its technical depth, as the gist of the paper can be summarized as the following: instead of keeping the batch norm mean and bias estimation over the whole model, estimate them on a per-domain basis. I am not sure if this is novel, as this is a natural extension of the original batch normalization paper. Overall I think this paper is more fit as a short workshop presentation rather than a full conference paper. Detailed comments: Section 3.1: I respectfully disagree that the core idea of BN is to align the distribution of training data. It does this as a side effect, but the major purpose of BN is to properly control the scale of the gradient so we can train very deep models without the problem of vanishing gradients. It is plausible that intermediate features from different datasets naturally show as different groups in a t-SNE embedding. This is not the particular feature of batch normalization: visualizing a set of intermediate features with AlexNet and one gets the same results. So the premise in section 3.1 is not accurate. Section 3.3: I have the same concern as the other reviewer. It seems to be quite detatched from the general idea of AdaBN. Equation 2 presents an obvious argument that the combined BN-fully_connected layer forms a linear transform, which is true in the original BN case and in this case as well. I do not think it adds much theoretical depth to the paper. (In general the novelty of this paper seems low) Experiments: - section 4.3.1 is not an accurate measure of the "effectiveness" of the proposed method, but a verification of a simple fact: previously, we normalize the source domain features into a Gaussian distribution. the proposed method is explicitly normalizing the target domain features into the same Gaussian distribution as well. So, it is obvious that the KL divergence between these two distributions are closer - in fact, one is *explicitly* making them close. However, this does not directly correlate to the effectiveness of the final classification performance. - section 4.3.2: the sensitivity analysis is a very interesting read, as it suggests that only a very few number of images are needed to account for the domain shift in the AdaBN parameter estimation. This seems to suggest that a single "whitening" operation is already good enough to offset the domain bias (in both cases shown, a single batch is sufficient to recover about 80% of the performance gain, although I cannot get data for even smaller number of examples from the figure). It would thus be useful to have a comparison between these approaches, and also a detailed analysis of the effect from each layer of the model - the current analysis seems a bit thin.
[EX A]:
en