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: El artículo presenta recomendaciones prácticas para el desarrollo de software seguro. Se describen las mejores prácticas recomendadas para desarrollar software que sea proactivo ante los ataques, y se realiza un análisis de costos de estas prácticas en desarrollo de software. Todo basado en una revisión de prácticas propuestas en la bibliografía y su contraste con datos obtenidos de una encuesta en empresas. Finalmente se recomienda una guía.  Sería ideal aplicar la guía propuesta a empresas no involucradas en la encuesta que sirvió para originarla de modo de poder evaluar su efectividad en forma independiente.

Output: es


Input: Consider Input: This paper provides a number of performance enhancements inspired by domain knowledge. Taken together, these produce a compelling system that has shown itself to be the best-in-class as per the related competition.  Experts agree that the authors do a good job at justifying the majority of the design decisions.    pros:  - insights into the SOTA Doom player    cons:  - lack of pure technical novelty: the various elements have existed previously    This paper comes down to a matter of taste in terms of appreciation of SOTA systems or technical novelty.  With the code being released, I believe that this work will have impact as a benchmark, and as a guidebook  as to how features can be combined for SOTA performance on FPS-style scenarios.

Output: en


Input: Consider Input: This work proposes a model that can learn short binary codes via paragraph vectors to allow fast retrieval of documents. The experiments show that this is superior to semantic hashing. The approach is simple and not very technically interesting. For a code size of 128, the loss compared to a continuous paragraph vector seems moderate.  The paper asks the reader to refer to the Salakhutdinov and Hinton paper for the baseline numbers but I think they should be placed in the paper for easy reference. For simplicity, the paper could show the precision at 12.5%, 25% and 50% recall for the proposed model and semantic hashing. It also seems that the semantic hashing paper shows results on RCV2 and not RCV1. RCV1 is twice the size of RCV2 and is English only so it seems that these results are not comparable. It would be interesting to see how many binary bits are required to match the performance of the continuous representation. A comparison to the continuous PV-DBOW trained with bigrams would also make it a more fair comparison.  Figure 7 in the paper shows a loss from using the real-binary PV-DBOW. It seems that if a user needed high quality ranking after the retrieval stage and they could afford the extra space and computation, then it would be better for them to use a standard PV-DBOW to obtain the continuous representation at that stage.  Minor comments: First line after the introduction: is sheer -> is the sheer 4th line from the bottom of P1: words embeddings -> word embeddings In table 1: What does code size refer to for PV-DBOW? Is this the number of elements in the continuous vector? 5th line from the bottom of P5: W -> We 5th line after section 3.1: covers wide -> covers a wide


Output: en