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: Abstract: Needs to have a definition of ERP - can't assume the reader knows what this means.  Intro: Avoid 1 sentence paragraphs (page 1) The introduction is rather long - it seems to actually be two different sections: an introduction (1st four paragraphs) and 1+ page of background and hypothesis. Overall - at 1.5 pages the intro is rather long for a paper that is 4 pages total.  Methodology: I think there are a lot of assumptions in regards to what ERP are and how they work.  While the paper is a statistical study, it would have benefited with a context of an actual example.  The samples are from small to medium size companies (how many?) with 49 use case (how do these relate?).  Results: Discussion is too limited - it assumes that a reader is very familiar with the area, and that may not be the case.

Output: en


Input: Consider Input: This paper proposes an idea of looking n-steps backward when modelling sequences with RNNs. The proposed RNN does not only use the previous hidden state (t-1) but also looks further back ( (t - k) steps, where k=1,2,3,4 ). The paper also proposes a few different ways to aggregate multiple hidden states from the past.   The reviewer can see few issues with this paper.  Firstly, the writing of this paper requires improvement. The introduction and abstract are wasting too much space just to explain unrelated facts or to describe already well-known things in the literature. Some of the statements written in the paper are misleading. For instance, it explains, “Among various neural network models, recurrent neural networks (RNNs) are appealing for modeling sequential data because they can capture long term dependency in sequential data using a simple mechanism of recurrent feedback” and then it says RNNs cannot actually capture long-term dependencies that well. RNNs are appealing in the first place because they can handle variable length sequences and can model temporal relationships between each symbol in a sequence. The criticism against LSTMs is hard to accept when it says: LSTMs are slow and because of the slowness, they are hard to scale at larger tasks. But we all know that some companies are already using gigantic seq2seq models for their production (LSTMs are used as building blocks in their systems). This indicates that the LSTMs can be practically used in a very large-scale setting.   Secondly, the idea proposed in the paper is incremental and not new to the field. There are other previous works that propose to use direct connections to the previous hidden states [1]. However, the previous works do not use aggregation of multiple number of previous hidden states. Most importantly, the paper fails to deliver a proper analysis on whether its main contribution is actually helpful to improve the problem posed in the paper. The new architecture is said that it handles the long-term dependencies better, however, there is no rigorous proof or intuitive design in the architecture that help us to understand why it should work better. By the design of the architecture, and speaking in very high-level, it seems like the model maybe helpful to mitigate the vanishing gradients issue by a linear factor. It is always a good practice to have at least one page to analyze the empirical findings in the paper.   Thirdly, the baseline models used in this paper are very weak. Their are plenty of other models that are trained and tested on word-level language modelling task using Penn Treebank corpus, but the paper only contains a few of outdated models. I cannot fully agree on the statement “To the best of our knowledge, this is the best performance on PTB under the same training condition”, these days, RNN-based methods usually score below 80 in terms of the test perplexity, which are far lower than 100 achieved in this paper.   [1] Zhang et al., “Architectural Complexity Measures of Recurrent Neural Networks”, NIPS’16

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