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].

Input: Consider Input: Strengths:  - Innovative idea: sentiment through regularization - Experiments appear to be done well from a technical point of view - Useful in-depth analysis of the model  Weaknesses:  - Very close to distant supervision - Mostly poorly informed baselines  General Discussion:  This paper presents an extension of the vanilla LSTM model that incorporates sentiment information through regularization.  The introduction presents the key claims of the paper: Previous CNN approaches are bad when no phrase-level supervision is present. Phrase-level annotation is expensive. The contribution of this paper is instead a "simple model" using other linguistic resources.  The related work section provides a good review of sentiment literature. However, there is no mention of previous attempts at linguistic regularization (e.g., [YOG14]).  The explanation of the regularizers in section 4 is rather lengthy and repetitive. The listing on p. 3 could very well be merged with the respective subsection 4.1-4.4. Notation in this section is inconsistent and generally hard to follow. Most notably, p is sometimes used with a subscript and sometimes with a superscript.  The parameter \beta is never explicitly mentioned in the text. It is not entirely clear to me what constitutes a "position" t in the terminology of the paper. t is a parameter to the LSTM output, so it seems to be the index of a sentence. Thus, t-1 is the preceding sentence, and p_t is the prediction for this sentence. However, the description of the regularizers talks about preceding words, not sentences, but still uses. My assumption here is that p_t is actually overloaded and may either mean the sentiment of a sentence or a word. However, this should be made clearer in the text.  One dangerous issue in this paper is that the authors tread a fine line between regularization and distant supervision in their work. The problem here is that there are many other ways to integrate lexical information from about polarity, negation information, etc. into a model (e.g., by putting the information into the features). The authors compare against a re-run or re-implementation of Teng et al.'s NSCL model. Here, it would be important to know whether the authors used the same lexicons as in their own work. If this is not the case, the comparison is not fair. Also, I do not understand why the authors cannot run NSCL on the MR dataset when they have access to an implementation of the model. Would this not just be a matter of swapping the datasets? The remaining baselines do not appear to be using lexical information, which makes them rather poor. I would very much like to see a vanilla LSTM run where lexical information is simply appended to the word vectors.  The authors end the paper with some helpful analysis of the models. These experiments show that the model indeed learns intensification and negation to some extent. In these experiments, it would be interesting to know how the model behaves with out-of-vocabulary words (with respect to the lexicons). Does the model learn beyond memorization, and does generalization happen for words that the model has not seen in training? Minor remark here: the figures and tables are too small to be read in print.  The paper is mostly well-written apart from the points noted above.  It could benefit from some proofreading as there are some grammatical errors and typos left. In particular, the beginning of the abstract is hard to read.  Overall, the paper pursues a reasonable line of research. The largest potential issue I see is a somewhat shaky comparison to related work. This could be fixed by including some stronger baselines in the final model. For me, it would be crucial to establish whether comparability is given in the experiments, and I hope that the authors can shed some light on this in their response.  [YOG14] [Link]  --------------  Update after author response  Thank you for clarifying the concerns about the experimental setup.   NSCL: I do now believe that the comparison is with Teng et al. is fair.  LSTM: Good to know that you did this. However, this is a crucial part of the paper. As it stands, the baselines are weak. Marginal improvement is still too vague, better would be an open comparison including a significance test.  OOV: I understand how the model is defined, but what is the effect on OOV words? This would make for a much more interesting additional experiment than the current regularization experiments.

Output: en


Input: Consider Input: - Strengths:  This paper tackles an interesting problem and provides a (to my knowledge) novel and reasonable way of learning and combining cognitive features with textual features for sentiment analysis and irony detection. The paper is  clearly written and organized, and the authors provided a lot of useful detail and informative example and plots. Most of the results are convincing, and the authors did a good job comparing their approach and results with previous work.  - Weaknesses:  1. Just from the reading abstract, I expected that the authors' approach would significantly outperform previous methods, and that using both the eye-gaze and textual features consistently yields the best results. Upon reading the actual results section, however, it seems like the findings were more mixed. I think it would be helpful to update the abstract and introduction to reflect this.  2. When evaluating the model on dataset 1 for sentiment analysis, were the sarcastic utterances included? Did the model do better on classifying the non-sarcastic utterances than the sarcastic ones? 3. I understand why the eye-movement data would be useful for sarcasm detection, but it wasn't as obvious to me why it would be helpful for (non-sarcastic) sentiment classification beyond the textual features.   - General Discussion:  This paper contains a lot of interesting content, and the approach seems solid and novel to me. The results were a little weaker than I had anticipated from the abstract, but I believe would still be interesting to the larger community and merits publication.

Output: en


Input: Consider Input: Building on earlier work on a model called NICE, this paper presents an approach to constructing deep feed-forward generative models. The model is evaluated on several datasets. While it does not achieve state-of-the-art performance, it advances an interesting class of models. The paper is mostly well written and clear.  Given that inference and generation are both efficient and exact, and given that this represents a main advantage over other models, it would be great if the authors could provide some motivating example applications where this is needed/would be useful.  The authors claim that “unlike both variational autoencoders and GANs, our technique is able to learn a semantically meaningful latent space which is as high dimensional as the input space.” Where is the evidence for this claim? I didn’t see any analysis of the semantic meaningfulness of the latent space learned by real NVP. Stronger evidence that the learned representations are actually useful for downstream tasks would be nice.  I still think the author’s intuitions around the “fixed reconstruction cost of L2” are very vague. The factorial Gaussian assumption itself does not limit the generative model, it merely smoothes an otherwise arbitrary distribution, and to a degree which can be arbitrarily small, p(x) = \int p(z) N(x | f(z), \sigma^2) dz. How a lose lower bound plays into this is not clear from the paper.


Output: en