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].

Input: Consider 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.

Output: en


Input: Consider Input: Overall, this is a nice paper. Developing a unifying framework for these newer neural models is a worthwhile endeavor.  However, it's unclear if the DRAGNN framework (in its current form) is a significant standalone contribution. The main idea is straightforward: use a transition system to unroll a computation graph. When you implement models in this way you can reuse code because modules can be mixed and matched. This is nice, but (in my opinion) is just good software engineering, not machine  learning research.  Moreover, there appears to be little incentive to use DRAGNN, as there are no 'free things' (benefits) that you get by using the framework. For example:  - If you write your neuralnet in an automatic differentiation library (e.g.,   tensorflow or dynet) you get gradients for 'free'.  - In the VW framework, there are efficiency tricks that 'the credit assignment   compiler' provides for you, which would be tedious to implement on your   own. There is also a variety of algorithms for training the model in a   principled way (i.e., without exposure bias).  I don't feel that my question about the limitations of the framework has been satisfactorily addressed. Let me ask it in a different way: Can you give me examples of a few models that I can't (nicely) express in the DRAGNN framework? What if I wanted to implement

Output: en


Input: Consider Input: CONTRIBUTIONS When training LSTMs, many of the intermediate gradients are close to zero due to the flat shape of the tanh and sigmoid nonlinearities far from the origin. This paper shows that rounding these small gradients to zero results in matrices with up to 80% sparsity during training, and that training character-level LSTM language models with this sparsification does not significantly change the final performance of the model. The authors argue that this sparsity could be exploited with specialized hardware to improve the energy efficiency and speed of recurrent network training.  NOVELTY Thresholding gradients to induce sparsity and improve efficiency in RNN training is a novel result to my knowledge.  MISSING CITATIONS Prior work has explored low-precision arithmetic for recurrent neural network language models:  Hubara et al, “Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations”,


Output: en