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: - Strengths:  The authors present a novel adaptation of encoder-decoder neural MT using an approach that starts and ends with characters, but in between works with representations of morphemes and characters.   The authors release both their code as well as their final learned models for fr-en, cs-en, and en-cs. This is helpful in validating their work, as well as for others looking to replicate and extends this work.  The system reported appears to produce translation results of reasonable quality even after the first training epoch, with continued progress in future epochs.  The system appears to learn reasonable morphological tokenizations, and appears able to handle previously unseen words (even nonce words) by implicitly backing off to morphemes.  - Weaknesses:  In the paper, the authors do not explicitly state which WMT test and dev sets their results are reported on. This is problematic for readers wishing to compare the reported results to existing work (for example, the results at matrix.statmt.org). The only way this reviewer found to get this information was to look in the README of the code supplement, which indicates that the test set was newstest2015 and the dev test was newstest2013. This should have been explicitly described in the paper.  The instructions given in the software README are OK, but not great. The training and testing sections each could be enhanced with explicit examples of how to run the respective commands. The software itself should respond to a --help flag, which it currently does not.  The paper describes a 6-level architecture, but the diagram in Figure 2 appears to show fewer than 6 layers. What's going on? The caption should be more explicit, and if this figure is not showing all of the layers, then there should be a figure somewhere (even if it's in an appendix) showing all of the layers.  The results show comparison to other character-based neural systems, but do not show state-of-the-art results for other types of MT system. WMT (and matrix.statmt.org) has reported results for other systems on these datasets, and it appears that the state-of-the-art is much higher than any of the results reported in this paper. That should be acknowledged, and ideally should be discussed.  There are a handful of minor English disfluencies, misspellings, and minor LaTeX issues, such as reverse quotation marks. These should be corrected.  - General Discussion:  Paper is a nice contribution to the existing literature on character-based neural MT.
Example Output: en

Example Input: Contributions The paper presents an adaptation of batch normalization for RNNs in the case of LSTMs, along the horizontal depth. Contrary to previous work from (Laurent 2015; Amodei 2016), the work demonstrates that batch-normalizing the hidden states of RNNs can improve optimization, and argues with quantitative experiments that the key factor to making this work is proper initialization of parameters, in particular gamma. Experiments show some gain in performance over vanilla LSTMs on Sequential MNIST, PTB, Text8 and CNN Question-Answering.  Novelty+Significance Batch normalization has been key for training deeper and deeper networks (e.g. ResNets) and it seems natural that we would want to extend it to RNNs.  The paper shows that it is possible to do so with proper initialization of parameters, contrary to previous work from (Laurent 2015; Amodei 2016). Novelty comes from where to batch norm (i.e. not in the cell update) and in the per-time step statistics.   Adding batch normalization to LSTMs incurs additional computational cost and bookkeeping; for training speed comparisons (e.g. Figure 2) the paper only compares LSTM and BN-LSTM by iteration count; given the additional complexity of the BN-LSTM I would have also liked to see a wall-clock comparison.  As RNNs are used across many tasks, this work is of interest to many.  However, the results gains are generally minor and require several tricks to work in practice. Also, this work doesn’t address a question about batch normalization that it seems natural that it helps with faster training, but why would it also improve generalization?   Clarity The paper is overall very clear and well-motivated. The model is well described and easy to understand, and the plots illustrate the points clearly.  Summary Interesting though relatively incremental adaptation, but shows batch normalization to work for RNNs where previous works have not succeeded. Comprehensive set of experiments though it is questionable if the empirical gains are significant enough to justify the increased model complexity as well as computational overhead.  Pros - Shows batch normalization to work for RNNs where previous works have not succeeded - Good empirical analysis of hyper-parameter choices and of the activations - Experiments on multiple tasks - Clarity  Cons - Relatively incremental - Several ‘hacks’ for the method (per-time step statistics, adding noise for exploding variance, sequence-wise normalization) - No mention of computational overhead - Only character or pixel-level tasks, what about word-level?
Example Output: en

Example Input: Authors propose a competitive learning architecture that learn different RNN predictors independently, akin to a committee of experts which are chosen with a hard switch at run-time. This work is applied to the task of predictive different driving behaviors from human drivers, and combines behaviors at test time, often switching behaviors within seconds. Prediction loss is lower than the similar but non-competitive architecture used as a baseline. It is not very clear how to interpret the results, what is the real impact of the model. If behaviors switch very often, can this really be seen as choosing the best driving mode for a given situation? Maybe the motivation needs to be rephrased a little to be more convincing? The competitive approach presented is interesting but not really novel, thus the impact of this paper for a conference such as ICLR may be limited.
Example Output:
en