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: The authors describe a dataset of proof steps in higher order logic derived from a set of proven theorems. The success of methods like AlphaGo suggests that for hard combinatorial style problems, having a curated set of expert data (in this case the sequence of subproofs) is a good launching point for possibly super-human performance. Super-human ATPs are clearly extremely valuable. Although relatively smaller than the original Go datasets, this dataset seems to be a great first step. Unfortunately, the ATP and HOL aspect of this work is not my area of expertise. I can't comment on the quality of this aspect. It would be great to see future work scale up the baselines and integrate the networks into state of the art ATPs. The capacity of deep learning methods to scale and take advantage of larger datasets means there's a possibility of an iterative approach to improving ATPs: as the ATPs get stronger they may generate more data in the form of new theorems. This may be a long way off, but the possibility is exciting.
Example Output: en

Example Input: *** Paper Summary *** This paper formalizes the properties required for addressing (indexing) memory augmented neural networks as well as how to pair the addressing with read/write operation. It then proposes a framework in which any Lie group as the addressing space. Experiments on algorithmic tasks are reported. *** Review Summary *** This paper brings unity and formalism in the requirement for memory addressing while maintaining differentiable memories. Its proposal provide a generic scheme to build addressing mechanisms. When comparing the proposed approach with key-value networks, the unbounded number of memory cells and the lack of incentive to reuse indexes might reveal impractical. *** Detailed Review *** The paper reads well, has appropriate relevance to related work. The unified presentation of memory augmented networks is clear and brings unity to the field. The proposed approach is introduced clearly, is powerful and gives a tool that can be reused after reading the article. I do not appreciate that the growing memory is not mentioned as a drawback. It should be stressed and a discussion on the impact it has on efficiency/scalability is needed.
Example Output: en

Example Input: This paper introduces the concept of fuzzy paraphrases to aid in the learning of distributed word representations from a corpus augmented by a lexicon or ontology. Sometimes polysemy is context-dependent, but prior approaches have neglected this fact when incorporating external paraphrase information during learning. The main idea is to introduce a function that essentially judges the context-sensitivity of paraphrase candidates, down-weighting those candidates that depend strongly on context. This function is inferred from bilingual translation agreement. The main argumentation leading to the model selection is intuitive, and I believe that the inclusion of good paraphrases and the elimination of bad paraphrases during training should in principle improve word representation quality. However, the main questions are how well the proposed method achieves this goal, and, even if it achieves it well, whether it makes much difference in practical terms. Regarding the first question, I am not entirely convinced that the parameterization of the control function f(x_ij) is optimal. It would have been nice to see some experiments investigating different choices, in particular some baselines where the effect of f is diminished (so that it reduces to f=1 in the limit) would have been interesting. I also feel like there would be a lot to gain from having f be a function of the nearby word embeddings, though this would obvious incur a significant slowdown. (See for example 'Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space' by Neelakantan et al, which should probably be cited.) As it stands, the experimental results do not clearly distinguish the fuzzy paraphrase approach from prior work, i.e. tables 3 and 4 do not show major trends one way or the other. Regarding the second question, it is hard to draw many conclusions from analogy tasks alone, especially when effects unrelated to good/bad paraphrasing such as corpus size/content, window size, vocabulary size, etc., can have an outsize effect on performance. Overall, I think this is a good paper presenting a sensible idea, but I am not convinced by the experiments that the specific approach is achieving its goal. With some improved experiments and analysis, I would wholeheartedly recommend this paper for acceptance; as it stands, I am on the fence.
Example Output:
en