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]: Si bien es cierto el trabajo se encuentra bien estructurado y da muestras de una metodologia adecuada, no queda clara la utilidad del estudio realizado. Los resultados obtenidos y presentados, se basan en un periodo de estudio muy reducido, lo cual quita confianza a las conclusiones. Se observan algunos pequenos errores de edicion y redaccion en el texto. Se recomienda ampliar el conjunto de datos de entrada del estudio.
[EX A]: es

[EX Q]: The following statement best summarizes the contribution: "This paper shows that model free RL methods can learn how to gather information about physical properties of objects, even when this information is not available to a passive observer, and use this information to make decisions." So this is not a paper about new theory or algorithms, but rather about solving the problem of acquiring knowledge about the physics of the world around us, which is important for many problems and helps explain human performance in many tasks. There are still some concerns about the depth-of-analysis of the paper, but on balance, it is seen as an unconventional but interesting paper. As per AnonReviewer6, the final version could still aim to better address "What should other researchers focus on if they are trying to build agents that can understand physics intuitively (building off this work)?" -- Area chair
[EX A]: en

[EX Q]: 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",
[EX A]:
en