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].
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Question: El trabajo presenta un estudio del estado del arte en lo que respecta a sistemas de robótica cooperativa de multi-robots. El foco de la revisión es la arquitectura de control presente en aquellos sistemas.  El abstract en inglés debe ser revisado en su redacción, en especial el uso de palabras, como "giving"->"providing", "panoramic"->"general", etc.  La introducción no entrega un mayor aporte a la estructura del documento. Se habla un poco de la investigación asociada al tema, pero con un uso muy bajo en referencias y la sección termina abruptamente sin explicar metodología o contribuciones del trabajo.  El trabajo entrega un "planteamiento del problema" muy poco claro y que no provee mayor relevancia. Más aun, además de identificar algunas aproximaciones al contexto, el trabajo no plantea ninguna solución, alternativa, categorización, marco de trabajo o aporte en general al (poco claro) problema presentado.  En resumen, el trabajo realiza una recopilación relativamente adecuada de información respecto a sistemas de robótica cooperativa de multi-robots, sin embargo, más allá de eso, no se revela un real aporte o relevancia desde el punto de vista científico al trabajo. Si estos puntos fueran más claros, si se presentara de forma directa la metodología, y si se presentara un producto concreto de investigación, este trabajo podría ser aceptado, pero en su estado actual no se recomienda.

Answer: es


Question:  It has recently come to my attention that the objective proposed by the authors in this paper has in fact already been studied in the literature under the name 'reward-weighted regression', from e.g. ICML 2007 [1]. This has spawned several other works using the same objective (e.g. [2]). One can examine for instance the objective proposed in Section 3.4 from [2], from ICANN 2008.  While this paper has already been accepted to ICLR, it would be beneficial for the authors to at least cite these works (and other related works) so that readers are aware of the previous origins of this idea.  [1] 

Answer: en


Question: This paper proposes to use an encoder-decoder framework for keyphrase generation. Experimental results show that the proposed model outperforms other baselines if supervised data is available.  - Strengths: The paper is well-organized and easy to follow (the intuition of the proposed method is clear). It includes enough details to replicate experiments. Although the application of an encoder-decoder (+ copy mechanism) is straightforward, experimental results are reasonable and support the claim (generation of absent keyphrases) presented in this paper.  - Weaknesses: As said above, there is little surprise in the proposed approach. Also, as described in Section 5.3, the trained model does not transfer well to new domain (it goes below unsupervised models). One of the contribution of this paper is to maintain training corpora in good quantity and quality, but it is not (explicitly) stated.  - General Discussion: I like to read the paper and would be pleased to see it accepted. I would like to know how the training corpus (size and variation) affects the performance of the proposed method. Also, it would be beneficial to see the actual values of p_g and p_c (along with examples in Figure 1) in the CopyRNN model. From my experience in running the CopyNet, the copying mechanism sometimes works unexpectedly (not sure why this happens).

Answer:
en