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:
The paper presents a way to "learn" approximate data structures. They train neural networks (ConvNets here) to perform as an approximate abstract data structure by having an L2 loss (for the unrolled NN) on respecting the axioms of the data structure they want the NN to learn. E.g. you NN.push(8), NN.push(6), NN.push(4), the loss is proportional to the distance with what is NN.pop()ed three times and 4, 6, 8 (this example is the one of Figure 1). There are several flaws: - In the case of the stack: I do not see a difference between this and a seq-to-seq RNN trained with e.g. 8, 6, 4 as input sequence, to predict 4, 6, 8. - While some of the previous work is adequately cited, there is an important body of previous work (some from the 90s) on learning Peano's axioms, stacks, queues, etc. that is not cited nor compared to. For instance [Das et al. 1992], [Wiles & Elman 1995], and more recently [Graves et al. 2014], [Joulin & Mikolov 2015], [Kaiser & Sutskever 2016]... - Using MNIST digits, and not e.g. a categorical distribution on numbers, is adding complexity for no reason. - (Probably the biggest flaw) The experimental section is too weak to support the claims. The figures are adequate, but there is no comparison to anything. There is also no description nor attempt to quantify a form of "success rate" of learning such data structures, for instance w.r.t the number of examples, or w.r.t to the size of the input sequences. The current version of the paper (December 9th 2016) provides, at best, anecdotal experimental evidence to support the claims of the rest of the paper. While an interesting direction of research, I think that this paper is not experimentally sound enough for ICLR.

Ex Output:
en

Ex Input:
Este articulo presenta una comparacion, en base a una lista de criterios, de dos enfoques de bases de datos: relacional vs. temporal. El articulo en general tiene un orden logico y es legible. No obstante, la comparativa presenta al menos dos inconvenientes: 1) falta mucha informacion de como se obtuvieron los resultados de las tablas 3 y 4. Debe existir una descripcion suficientemente detallada para luego poder replicar el trabajo. 2) El caso de estudio que se presenta forma la base de la comparacion. Una real comparacion debe sustentarse en una variedad de casos y dominios, para poder luego concluir con una base mas amplia. Sugiero abordar estos dos aspectos primero para sustentar mejor los resultados y conclusiones.

Ex Output:
es

Ex Input:
This paper presents a set of experiments investigating what kinds of information are captured in common unsupervised approaches to sentence representation learning. The results are non-trivial and somewhat surprising. For example, they show that it is possible to reconstruct word order from bag of words representations, and they show that LSTM sentence autoencoders encode interpretable features even for randomly permuted nonsense sentences. Effective unsupervised sentence representation learning is an important and largely unsolved problem in NLP, and this kind of work seems like it should be straightforwardly helpful towards that end. In addition, the experimental paradigm presented here is likely more broadly applicable to a range of representation learning systems. Some of the results seem somewhat strange, but I see no major technical concerns, and think that that they are informative. I recommend acceptance. One minor red flag: - The massive drop in CBOW performance in Figures 1b and 4b are not explained, and seem implausible enough to warrant serious further investigation. Can you be absolutely certain that those results would appear with a different codebase and different random seed implementing the same model? Fortunately, this point is largely orthogonal to the major results of the paper. Two writing comments: - I agree that the results with word order and CBOW are surprising, but I think it's slightly misleading to say that CBOW is predictive of word order. It doesn't represent word order at all, but it's possible to probabilistically reconstruct word order from the information that it does encode. - Saying that "LSTM auto-encoders are more effective at encoding word order than word content" doesn't really make sense. These two quantities aren't comparable.

Ex Output:
en