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:
This is an interesting paper about quantized networks that work on temporal difference inputs.  The basic idea is that when a network has only to process differences then this is computational much more efficient specifically with natural video data since large parts of an image would be fairly constant so that the network only has to process the informative sections of the image (video stream). This is of course how the human visual system works, and it is hence of interest even beyond the core machine learning community.   As an aside, there is a strong community interested in event-based vision such as the group of Tobi Delbrück, and it might be interesting to connect to this community. This might even provide a reference for your comments on page 1.  I guess the biggest novel contribution is that a rounding network can be replaced by a sigma-delta network, but that the order of discretization and summation doe make some difference in the actual processing load. I think I followed the steps and  Most of my questions have already been answers in the pre-review period. My only question remaining is on page 3, “It should be noted that when we refer to “temporal differences”, we refer not to the change in the signal over time, but in the change between two inputs presented sequentially. The output of our network only depends on the value and order of inputs, not on the temporal spacing between them.”  This does not make sense to me. As I understand you just take the difference between two frames regardless if you call this temporal or not it is a change in one frame. So this statement rather confuses me and maybe should be dropped unless I do miss something here, in which case some more explanation would be necessary.  Figure 1 should be made bigger.  An improvement of the paper that I could think about is a better discussion of the relevance of the findings. Yes, you do show that your sigma-delta network save some operation compared to threshold, but is this difference essential for a specific task, or does your solution has relevance for neuroscience?

Ex Output:
en


Ex Input:
Un muy buen artículo. Solo sugiero lo siguiente:  Las tablas en la sección de resultados deben ser explicadas.  Las tablas solo contienen el menor, mayor y media entre esos dos valores. Sería interesante considerar también la media general y la desviación estándar.  En las tablas 4, 5 y 6 solo algunas líneas contienen un valor destacado, las veces el mayor de la línea y otras el menor... Explicar mejor el significado de esas marcaciones en todas las tablas.  La referencia [12] comienza con "and" y no con el nombre del primer  autor (de echo parece que están con problemas también el resto de los autores).  La referencia [22] es la única que no usa iniciales para el nombre del autor.

Ex Output:
es


Ex Input:
1.	Lo que se propone es interesante como una aplicación pero no es un trabajo científico. Además ya no es interesante la aplicación de un Datamart desde el punto de vista técnico como es presentado por el autor pues existe mucha documentación y trabajos al respecto. Por este motivo ahora hay que buscar "otros aspectos (diseño, metodología, etc.)" que hagan interesante un artículo del área Datamart.  2. Hay errores de diseño, por lo cual lo más probable es que los resultados que se muestran estén erróneos.  3. Debe tener cuidado con la sumarización.

Ex Output:

es