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].

[EX Q]: En esta investigación se trata un tema que cada vez es más importante en ámbitos académicos y empresariales. Por otra parte, se utilizan métodos de análisis de datos complejos y muy adecuados a los objetivos de la investigación y los autores los que sirven de referencia básica para la investigación son muy adecuados.  En la medida en que el objeto material de la investigación es el individuo, entiendo que, cuando se describe la muestra, es conveniente, no sólo describir el perfil de la empresa, sino el perfil del individuo entrevistado (edad, sexo, puesto que ocupa, etc.). •	El apartado conclusiones merece, a mi juicio, una mayor atención, que incluya no sólo los resultados de la investigación, sino una discusión más amplia. •	Las limitaciones de la investigación hacen referencia al tamaño de la muestra y al tipo de muestreo empleado, pero no hacen referencia a la dimensión del modelo. En este sentido, sería conveniente plantear, como línea de investigación futura, la ampliación del modelo con nuevas variables e indicadores •	las referencias bibliográficas son anteriores al año 2009. Sugiero consultar las siguientes o	The effect of organizational support on ERP implementation DonHee Lee,  Sang M. Lee, David L. Olson, Soong Hwan Chung.  Industrial Management + Data Systems.  Wembley:2010.  Vol. 110,  Iss. 2,  p. 269-283 o	Predicting the behavioral intention to use enterprise resource planning systems :An exploratory extension of the technology acceptance model Fethi Calisir,  Cigdem Altin Gumussoy,  Armagan Bayram.  Management Research News.  Patrington:2009.  Vol. 32,  Iss. 7,  p. 597-613 o	Organizational adoption of information technologies: Case of enterprise resource planning systems Onur Kerimoglu,  Nuri Basoglu,  Tugrul Daim.  Journal of High Technology Management Research.  Greenwich:2008.  Vol. 19,  Iss. 1,  p. 21
[EX A]: es

[EX Q]: - Strengths: A well written paper, examining the use of context in lexical entailment task is a great idea, a well defined approach and experimental set-up and good analysis of the results   - Weaknesses: Some information is missing or insufficient, e.g., the table captions should be more descriptive, a clear description for each of the word type features should be given.  General Discussion:   The paper presents a proposal of consideration of context in lexical entailment task. The results from the experiments demonstrate that context-informed models do better than context-agnostic models on the entailment task.   I liked the idea of creating negative examples to get negative annotations automatically in the two ways described in the paper based on WordNet positive examples. (new dataset; an interesting method to develop dataset)  I also liked the idea of transforming already-used context-agnostic representations into contextualized representations, experimenting with different ways to get contextualized representations (i.e., mask vs contetx2vec), and testing the model on 3 different datasets (generalizability not just across different datasets but also cross-linguistically).  Motivations for various decisions in the experimental design were good to see, e.g., why authors used the split they used for CONTEXT-PPDB (it showed that they thought out clearly what exactly they were doing and why).  Lines 431-434: authors might want to state briefly how the class weights were determined and added to account for the unbalanced data in the CONTEXT-WN experiments. Would it affect direct comparisons with previous work, in what ways?   Change in Line 589: directionality 4 --> directionality, as in Table 4  Suggested change in Line 696-697: is-a hierarchy of WordNet --> "is-a" hierarchy of WordNet   For the sake of completeness, represent "mask" also in Figure 1.  I have read the author response.
[EX A]: en

[EX Q]: This work proposes to apply dilated convolutions for sequence tagging (specifically, named entity recognition). It also introduces some novel ideas (sharing the dilated convolution block, predicting the tags at each convolution level), which I think will prove useful to the community. The paper performs extensive ablation experiments to show the effectiveness of their approach. I found the writing to be very clear, and the experiments were exceptionally thorough.  Strengths:   - Extensive experiments against various architectures (LSTM, LSTM + CRF)        - Novel architectural/training ideas (sharing blocks)    Weaknesses:   - Only applied to English NER--this is a big concern since the title of the paper seems to reference sequence-tagging directly.   - Section 4.1 could be clearer. For example, I presume there is padding to make sure the output resolution after each block is the same as the input resolution.  Might be good to mention this.   - I think an ablation study of number of layers vs perf might be interesting.  RESPONSE TO AUTHOR REBUTTAL:  Thank you very much for a thoughtful response. Given that the authors have agreed to make the content be more specific to NER as opposed to sequence-tagging, I have revised my score upward.
[EX A]:
en