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].

Example Input: Combining storage and processing capabilities is an interesting research topic because data transfer is a major issue for many machine learning tasks. The paper itself is well-written, but unfortunately addresses a lot of things only to medium depth (probably due length constraints). My opinion is that a journal with an in-depth discussion of the technical details would be a better target for this paper.  Even though the researchers took an interesting approach to evaluate the performance of the system, it's difficult for me to grasp the expected practical improvements of this approach. With such a big focus on GPU (and more specialized hardware such as TPUs), the one question that comes to mind: By how much does this - or do you expect it to - beat the latest and greatest GPU on a real task?  I don't consider myself an expert on this topic even though I have some experience with SystemC.
Example Output: en

Example Input: - Strengths: This paper presents an extension of many popular methods for learning vector representations of text.  The original methods, such as skip-gram with negative sampling, Glove, or other PMI based approaches currently use word cooccurrence statistics, but all of those approaches could be extended to n-gram based statistics.  N-gram based statistics would increase the complexity of every algorithm because both the vocabulary of the embeddings and the context space would be many times larger.  This paper presents a method to learn embeddings for ngrams with ngram context, and efficiently computes these embeddings.  On similarity and analogy tasks, they present strong results.  - Weaknesses: I would have loved to see some experiments on real tasks where these embeddings are used as input beyond the experiments presented in the paper.  That would have made the paper far stronger.  - General Discussion: Even with the aforementioned weakness, I think this is a nice paper to have at ACL.  I have read the author response.
Example Output: en

Example Input: Important problem, simple (in a positive way) idea, broad experimental evaluation; all reviewers recommend accepting the paper, and the AC agrees. Please incorporate any remaining reviewer feedback.
Example Output:
en