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].

Q: - 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.

A: en
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Q: UPDATE: I have read the authors' rebuttal and also the other comments in this paper's thread. My thoughts have not changed.  The authors propose using a mixture prior rather than a uni-modal prior for variational auto-encoders. They argue that the simple uni-modal prior "hinders the overall expressivity of the learned model as it cannot possibly capture more complex aspects of the data distribution."  I find the motivation of the paper suspicious because while the prior may be uni-modal, the posterior distribution is certainly not. Furthermore, a uni-modal distribution on the latent variable space can certainly still lead to the capturing of complex, multi-modal data distributions. (As the most trivial case, take the latent variable space to be a uniform distribution; take the likelihood to be a point mass given by applying the true data distribution's inverse CDF to the uniform. Such a model can capture any distribution.)  In addition, multi-modality is arguably an overfocused concept in the literature, where the (latent variable) space is hardly anymore worth capturing from a mixture of simple distributions when it is often a complex nonlinear space. It is unclear from the experiments how much the influence of the prior's multimodality influences the posterior to capture more complex phenomena, and whether this is any better than considering a more complex (but still reparameterizable) distribution on the latent space.  I recommend that this paper be rejected, and encourage the authors to more extensively study the effect of different priors.  I'd also like to make two additional comments:  While there is no length restriction at ICLR, the 14 page document can be significantly condensed without loss of describing their innovation or clarity. I recommend the authors do so.  Finally, I think it's important to note the controversy in this paper. It was submitted with many significant incomplete details (e.g., no experiments, many missing citations, a figure placed inside that was pencilled in by hand, and several missing paragraphs). These details were not completed until roughly a week(?) later. I recommend the chairs discuss this in light of what should be allowed next year.

A: en
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Q: This paper is motivated by the ability that human's visual system can recognize contents of environment by from critical features, and tried to investigate whether neural networks can also have this kind of ability.  Specifically, the paper proposed to use Auto-Encoder (AE) as the network to reconstruct the low fidelity of visual input. Moreover, similar to Mnih et al. (2014),  the paper also proposed to use a recurrent fashion to mimic the sequential behavior the  human visual system.   I think the paper is well motivated. However, there are several concerns: 1. The baselines of the paper are too weak. Nearest neighbor, bilinear, bicubic and cubic interpolations without any learning procedure are of course performed worse than AE based models. The author should compare with the STOA methods such as

A:
en
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