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].

Example Input: Building on earlier work on a model called NICE, this paper presents an approach to constructing deep feed-forward generative models. The model is evaluated on several datasets. While it does not achieve state-of-the-art performance, it advances an interesting class of models. The paper is mostly well written and clear. Given that inference and generation are both efficient and exact, and given that this represents a main advantage over other models, it would be great if the authors could provide some motivating example applications where this is needed/would be useful. The authors claim that "unlike both variational autoencoders and GANs, our technique is able to learn a semantically meaningful latent space which is as high dimensional as the input space." Where is the evidence for this claim? I didn't see any analysis of the semantic meaningfulness of the latent space learned by real NVP. Stronger evidence that the learned representations are actually useful for downstream tasks would be nice. I still think the author's intuitions around the "fixed reconstruction cost of L2" are very vague. The factorial Gaussian assumption itself does not limit the generative model, it merely smoothes an otherwise arbitrary distribution, and to a degree which can be arbitrarily small, p(x) = \int p(z) N(x | f(z), \sigma^2) dz. How a lose lower bound plays into this is not clear from the paper.
Example Output: en

Example Input: The paper introduces an extension of the entity grid model. A convolutional neural network is used to learn sequences of entity transitions indicating coherence, permitting better generalisation over longer sequences of entities than the direct estimates of transition probabilities in the original model. This is a nice and well-written paper. Instead of proposing a fully neural approach, the authors build on existing work and just use a neural network to overcome specific issues in one step. This is a valid approach, but it would be useful to expand the comparison to the existing neural coherence model of Li and Hovy. The authors admit being surprised by the very low score the Li and Hovy model achieves on their task. This makes the reader wonder if there was an error in the experimental setup, if the other model's low performance is corpus-dependent and, if so, what results the model proposed in this paper would achieve on a corpus or task where the other model is more successful. A deeper investigation of these factors would strengthen the argument considerably. In general the paper is very fluent and readable, but in many places definite articles are missing (e.g. on lines 92, 132, 174, 488, 490, 547, 674, 764 and probably more). I would suggest proofreading the paper specifically with article usage in mind. The expression "...limits the model to do X...", which is used repeatedly, sounds a bit unusual. Maybe "limits the model's capacity to do X" or "stops the model from doing X" would be clearer. -------------- Final recommendation adjusted to 4 after considering the author response. I agree that objective difficulties running other people's software shouldn't be held against the present authors. The efforts made to test the Li and Hovy system, and the problems encountered in doing so, should be documented in the paper. I would also suggest that the authors try to reproduce the results of Li and Hovy on their original data sets as a sanity check (unless they have already done so), just to see if that works for them.
Example Output: en

Example Input: The paper presents a method to learn graph embeddings in a unsupervised way using random walks. It is well written and the execution appears quite accurate. The area of learning whole graph representations does not seem to be very well explored in general, and the proposed approach enjoys having very few competitors. In a nutshell, the idea is to linearize the graph using random walks and to compute the embedding of the central segment of each walk using the skip-thought criterion. Being not an expert in biology, I can not comment whether or not this makes sense, but the gains reported in Table 2 are quite significant. An anonymous public comment compared this work to a number of others in which the problem of learning representations of nodes is considered. While this is arguably a different goal, one natural baseline would be to pool these representations using mean- or max- pooling. It would very interesting to do such a comparison, especially given that the considered approach heavily relies on pooling (see Figure 3(c)) To sum up, I think it is a nice paper, and with more baselines I would be ready to further increase the numerical score.
Example Output:
en