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 paper takes a model based on that of Graves and retrofits it with a representation derived from the work of Plamondon.  part of the goal of deep learning has been to avoid the use of hand-crafted features and have the network learn from raw feature representations, so this paper is somewhat against the grain.   The paper relies on some qualitative examples as demonstration of the system, and doesn't seem to provide a strong motivation for there being any progress here.  The paper does not provide true text-conditional handwriting synthesis as shown in Graves' original work.   Be more consistent about your bibliography (e.g. variants of Plamondon's own name, use of "et al." in the bibliography etc.)

Ex Output:
en


Ex Input:
This is an interesting article, present develop of a mobile robot use as beach cleaner.  I have 2 comments about the design, I think will be interesting define what is the dimension of collector and storage compartment for know what is the capacity of this cleaner.  In page 2 in the first paragraph a typo mistake was found.

Ex Output:
en


Ex Input:
- Strengths: The paper addresses a relevant topic: learning the mapping between natural language and KB relations, in the context of QA (where we have only partial information for one of the arguments), and in the case of having a very large number of possible target relations.  The proposal consists in a new method to combine two different representations of the input text: a word level representation (i.e. with segmentation of the target relation names and also the input text), and relations as a single token (i.e without segmentation of relation names nor input text).   It seems, that the main contribution in QA is the ability to re-rank entities after the Entity Linking step.  Results show an improvement compared with the state of the art.   - Weaknesses: The approach has been evaluated in a limited dataset.   - General Discussion:  I think, section 3.1 fits better inside related work, so the 3.2 can become section 3 with the proposal. Thus, new section 3 can be splitted more properly.

Ex Output:

en