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: Es un trabajo difícil de catalogar, presenta un tema usualmente asociado a bases de datos deductivas (reparación), donde deben existir reglas o restricciones de integridad que han sido violadas, adaptado al tema de inconsistencia producida por nuevos hechos en un DW que no se ajustan correctamente a sus dimensiones. Esto, normalmente es tratado en este ámbito mediante el uso de Slowly Changing Dimensions (SCD), algo que extrañamente no es nombrado en el artículo.  Aun así, y asumiendo que el problema realmente existe y tiene aplicabilidad, es presentado muy superficialmente, y termina finalmente en una propuesta informal que no es descrita rigurosamente como un algoritmo en sí. Me parece que es una propuesta que aún se encuentra en un estado muy inicial y cuya problemática no logra convencerme como tal.

Output: es


Input: Consider Input: Strengths -- An interesting proposal for a smaller CNN architecture designed for embedded CNN applications.  -- Balanced exploration of CNN macroarchitecture and microarchitecture with fire modules. -- x50 less memory usage than AlexNet, keeping similar accuracy  -- strong experimental results  Weaknesses --Would be nice to test Sqeezenet on multiple tasks  --lack of insights and rigorous analysis into what factors are responsible for the success of SqueezeNet. For example, how are ResNet and GoogleNet connected to the current architecture? Another old paper (Analysis of correlation structure for a neural predictive model with application to speech recognition, Neural Networks, 1994) also showed that the “by-pass” architecture by mixing linear and nonlinear prediction terms improves long term dependency in NN based on rigorous perturbation analysis. Can the current work be placed more rigorously on theoretical analysis?

Output: en


Input: Consider Input: - Summary:   The paper introduces a new dataset for a sarcasm interpretation task and a system (called Sarcasm SIGN) based on machine translation framework Moses. The new dataset was collected from 3000 sarcastic tweets (with hashtag `#sarcasm) and 5 interpretations for each from humans. The Sarcasm SIGN is built based on Moses by replacing sentimental words by their corresponding clusters on the source side (sarcasm) and then de-cluster their translations on the target side (non-sarcasm). Sarcasm SIGN performs on par with Moses on the MT evaluation metrics, but outperforms Moses in terms of fluency and adequacy.   - Strengths:  the paper is well written  the dataset is collected in a proper manner  the experiments are carefully done and the analysis is sound.  - Weaknesses:  lack statistics of the datsets (e.g. average length, vocabulary size)  the baseline (Moses) is not proper because of the small size of the dataset  the assumption "sarcastic tweets often differ from their non sarcastic interpretations in as little as one sentiment word" is not supported by the data.   - General Discussion: This discussion gives more details about the weaknesses of the paper.   Half of the paper is about the new dataset for sarcasm interpretation. However, the paper doesn't show important information about the dataset such as average length, vocabulary size. More importantly, the paper doesn't show any statistical evidence to support their method of focusing on sentimental words.   Because the dataset is small (only 3000 tweets), I guess that many words are rare. Therefore, Moses alone is not a proper baseline. A proper baseline should be a MT system that can handle rare words very well. In fact, using clustering and declustering (as in Sarcasm SIGN) is a way to handle rare words.  Sarcasm SIGN is built based on the assumption that "sarcastic tweets often differ from their non sarcastic interpretations in as little as one sentiment word". Table 1 however strongly disagrees with this assumption: the human interpretations are often different from the tweets at not only sentimental words. I thus strongly suggest the authors to give statistical evidence from the dataset that supports their assumption. Otherwise, the whole idea of Sarcasm SIGN is just a hack.  --------------------------------------------------------------  I have read the authors' response. I don't change my decision because of the following reasons:   - the authors wrote that "the Fiverr workers might not take this strategy": to me it is not the spirit of corpus-based NLP. A model must be built to fit given data, not that the data must follow some assumption that the model is built on.  - the authors wrote that "the BLEU scores of Moses and SIGN are above 60, which is generally considered decent in the MT literature": to me the number 60 doesn't  show anything at all because the sentences in the dataset are very short. And that, if we look at table 6, %changed of Moses is only 42%, meaning that even more than half of the time translation is simply copying, the BLUE score is more than 60.  - "While higher scores might be achieved with MT systems that explicitly address rare words, these systems don't focus on sentiment words": it's true, but I was wondering whether sentiment words are rare in the corpus. If they are, those MT systems should obviously handle them (in addition to other rare words).


Output: en