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 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?

Ex Output:
en


Ex Input:
El trabajo presenta un report sobre aspectos de seguridad del producto software dependiendo de las tecnologías utilizadas para su desarrollo. Creo que falta rigor científico en la búsqueda de información. De hecho no es claramente explicado cómo se obtuvo la información por lo que queda como una monografía o marco teórico de un tema a investigar. No obstante, el estudio está bien presentado y su estructura es de calidad técnica. Creo que con una búsqueda en artículo de investigación puede ser mejorado.

Ex Output:
es


Ex Input:
- Strengths:   - The paper is clearly written and well-structured.    - The system newly applied several techniques including global optimization to end-to-end neural relation extraction, and the direct incorporation of the parser representation is interesting.   - The proposed system has achieved the state-of-the-art performance on both ACE05 and CONLL04 data sets.   - The authors include several analyses.  - Weaknesses:   - The approach is incremental and seems like just a combination of existing methods.     - The improvements on the performance (1.2 percent points on dev) are relatively small, and no significance test results are provided.  - General Discussion:  - Major comments:   - The model employed a recent parser and glove word embeddings. How did they affect the relation extraction performance?   - In prediction, how did the authors deal with illegal predictions?  - Minor comments:   - Local optimization is not completely "local". It "considers structural correspondences between incremental decisions," so this explanation in the introduction is misleading.   - Points in Figures 6 and 7 should be connected with straight lines, not curves.   - How are entities represented in "-segment"?   - Some citations are incomplete. Kingma et al. (2014) is accepted to ICLR, and Li et al. (2014) misses pages.

Ex Output:

en