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:
Ese trabajo muestra el procedimiento propuesto para la Identificación automática de fracturas en imágenes de pozos. La metodología se ve novedosa e interesante. El artículo muestra bien la metodología y tiene una buena redacción. Se agregaron métricas para medir la efectividad del algoritmo.  Este trabajo fue presentado en INFONOR 2014 y la redacción actual es la misma, que si bien es cierto es buena, presenta los mismos pequeños errores que en el artículo anterior. Por lo tanto, debe volver a revisarlo.  Aunque ahora muestra métricas para medir el resultado no se indica qué tan efectivo es este sistema respecto al utilizado antes.  Debe mejorar las figuras 12 y 15, en donde muestras las métricas. La figura 2, izquierda no se distingue. Falta incluir una referencia a la figura 11 en el texto.

Ex Output:
es


Ex Input:
The topic of the paper, model-based RL with a learned model, is important and timely. The paper is well written. I feel that the presented results are too incremental. Augmenting the frame prediction network with another head that predicts the reward is a very sensible thing to do. However neither the methodology not the results are novel / surprising, given that the original method of [Oh et al. 2015] already learns to successfully increment score counters in predicted frames in many games.  I’m very much looking forward to seeing the results of applying the learned joint model of frames and rewards to model-based RL as proposed by the authors.

Ex Output:
en


Ex Input:
In this paper, the authors propose a new method to learn hierarchical representations of sentences, based on reinforcement learning. They propose to learn a neural shift-reduce parser, such that the induced tree structures lead to good performance on a downstream task. They use reinforcement learning (more specifically, the policy gradient method REINFORCE) to learn their model. The reward of the algorithm is the evaluation metric of the downstream task. The authors compare two settings, (1) no structure information is given (hence, the only supervision comes from the downstream task) and (2) actions from an external parser is used as supervision to train the policy network, in addition to the supervision from the downstream task. The proposed approach is evaluated on four tasks: sentiment analysis, semantic relatedness, textual entailment and sentence generation.  I like the idea of learning tree representations of text which are useful for a downstream task. The paper is clear and well written. However, I am not convinced by the experimental results presented in the paper. Indeed, on most tasks, the proposed model is far from state-of-the-art models:  - sentiment analysis, 86.5 v.s. 89.7 (accuracy);  - semantic relatedness, 0.32 v.s. 0.25 (MSE);  - textual entailment, 80.5 v.s. 84.6 (accuracy). From the results presented in the paper, it is hard to know if these results are due to the model, or because of the reinforcement learning algorithm.  PROS:  - interesting idea: learning structures of sentences adapted for a downstream task.  - well written paper. CONS:  - weak experimental results (do not really support the claim of the authors).  Minor comments: In the second paragraph of the introduction, one might argue that bag-of-words is also a predominant approach to represent sentences. Paragraph titles (e.g. in section 3.2) should have a period at the end.  ---------------------------------------------------------------------------------------------------------------------- UPDATE  I am still not convinced by the results presented in the paper, and in particular by the fact that one must combine the words in a different way than left-to-right to obtain state of the art results. However, I do agree that this is an interesting research direction, and that the results presented in the paper are promising. I am thus updating my score from 5 to 6.

Ex Output:

en