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: The authors of this work propose a learnable approach to reducing the dimensionality of learned filters in deep neural networks. This is an interesting approach, but the presented work looks a bit raw.  1. There are many typos in this manuscript.  2. The experimental results are rather weak and don't show much improvement in accuracy. Instead the authors could position this work as a compression mechanism and would have to compare to low rank approximation of filters for DNNs. Yet this is not done.  3. Aside from compression, OMG can be viewed as a form of regularization to reduce the unnecessary capacity of the network to improve generalization. Again, this is not addressed in enough detail. 4. If the authors care to compare their approach to other 1-shot learning methods, then they would have to evaluate their approach with siamese and triplet learning networks. This isn't done.

Output: en


Input: Consider Input: El artículo está muy bien escrito y se lee con facilidad, teniendo básicamente características de un artículo de divulgación.  Se discuten aspectos centrados esencialmente en los problemas de recuperación de información ante el crecimiento exponencial de los datos. La propuesta realizada al final del artículo es interesante, aunque, desde mi punto de vista la inclusión del tema Big Data está dado con cierta liviandad y requiere un análisis más detallado de lo que se describe.

Output: es


Input: Consider Input: This paper compares different ways of inducing embeddings for the task of polarity classification. The authors focus on different types of corpora and find that not necessarily the largest corpus provides the most appropriate embeddings for their particular task but it is more effective to consider a corpus (or subcorpus) in which a higher concentration of subjective content can be found. The latter type of data are also referred to as "task-specific data". Moreover, the authors compare different embeddings that combine information from "task-specific" corpora and generic corpora. A combination outperforms embeddings just drawn from a single corpus. This combination is not only evaluated on English but also on a less resourced language (i.e. Catalan).  - Strengths: The paper addresses an important aspect of sentiment analysis, namely how to appropriately induce embeddings for training supervised classifers for polarity classification. The paper is well-structured and well-written. The major claims made by the authors are sufficiently supported by their experiments.  - Weaknesses: The outcome of the experiments is very predictable. The methods that are employed are very simple and ad-hoc. I found hardly any new idea in that paper. Neither are there any significant lessons that the reader learns about embeddings or sentiment analysis. The main idea (i.e. focusing on more task-specific data for training more accurate embeddings) was already published in the context of named-entity recognition by Joshi et al. (2015). The additions made in this paper are very incremental in nature.  I find some of the experiments inconclusive as (apparently) no statistical signficance testing between different classifiers has been carried out. In Tables 2, 3 and 6, various classifier configurations produce very similar scores. In such cases, only statistical signficance testing can really give a proper indication whether these difference are meaningful. For instance, in Table 3 on the left half reporting results on RT, one may wonder whether there is a significant difference between "Wikipedia Baseline" and any of the combinations. Furthermore, one doubts whether there is any signficant difference between the different combinations (i.e. either using "subj-Wiki", "subj-Multiun" or "subj-Europarl") in that table. The improvement by focusing on subjective subsets is plausible in general. However, I wonder whether in real life, in particular, a situation in which resources are sparse this is very helpful. Doing a pre-selection with OpinionFinder is some pre-processing step which will not be possible in most languages other than English. There are no equivalent tools or fine-grained datasets on which such functionality could be learnt. The fact that in the experiments for Catalan, this information is not considered proves that.   Minor details:  - lines 329-334: The discussion of this dataset is confusing. I thought the task is plain polarity classification but the authors here also refer to "opinion holder" and "opinion targets". If these information are not relevant to the experiments carried out in this paper, then they should not be mentioned here.  - lines 431-437: The variation of "splicing" that the authors explain is not very well motivated. First, why do we need this? In how far should this be more effective than simple "appending"?  - lines 521-522: How is the subjective information isolated for these configurations? I assume the authors here again employ OpinionFinder? However, there is no explicit mention of this here.  - lines 580-588: The definitions of variables do not properly match the formula (i.e. Equation 3). I do not find n_k in Equation 3.  - lines 689-695: Similar to lines 329-334 it is unclear what precise task is carried out. Do the authors take opinion holders and targets in consideration?  ***AFTER AUTHORS' RESPONSE*** Thank you very much for these clarifying remarks. I do not follow your explanations regarding the incorporation of opinion holders and targets, though.  Overall, I will not change my scores since I think that this work lacks sufficient novelty (the things the authors raised in their response are just insufficient to me). This submission is too incremental in nature.


Output: en