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 Input:
Los autores proponen el análisis de los datos a través de la metodología CRISP-DM, pero en el desarrollo no se utiliza el mismo.  Los autores hacen uso de la herramienta WEKA y de los algoritmo que tiene implementados dicha herramienta. Tal vez fuera mejor pensar en un algoritmo propio para este tipo de datos a analizar, o alguna mecanismo donde el experto no tenga que marcar las formas de los remolinos en las imágenes a procesar.

Ex Output:
es


Ex Input:
- Strengths: This paper proposes an evaluation metric for automatically evaluating the quality of dialogue responses in non-task-oriented dialogue. The metric operates on continuous vector space representations obtained by using RNNs and it comprises two components: one that compares the context and the given response and the other that compares a reference response and the given response. The comparisons are conducted by means of dot product after projecting the response into corresponding context and reference response spaces. These projection matrices are learned by minimizing the squared error between the model predictions and human annotations.  I think this work gives a remarkable step forward towards the evaluation of non-task-oriented dialogue systems. Different from previous works in this area, where pure semantic similarity was pursued, the authors are going beyond pure semantic similarity in a very elegant manner by learning projection matrices that transform the response vector into both context and reference space representations. I am very curious on how your projection matrices M and N differ from the original identity initialization after training the models. I think the paper will be more valuable if further discussion on this is introduced, rather than focusing so much on resulting correlations.   - Weaknesses:  The paper also leaves lots questions related to the implementation. For instance, it is not clear whether the human scores used to train and evaluate the system were single AMT annotations or the resulting average of few annotations. Also, it is not clear how the dataset was split into train/dev/test and whether n-fold cross validation was conducted or not. Also, it would be nice to better explain why in table 2 correlation for ADEM related scores are presented for the validation and test sets, while for the other scores they are presented for the full dataset and test set. The section on pre-training with VHRED is also very clumsy and confusing, probably it is better to give less technical details but a better high level explanation of the pre-training strategy and its advantages.  - General Discussion:  “There are many obvious cases where these metrics fail, as they are often incapable of considering the semantic similarity between responses (see Figure 1).” Be careful with statements like this one. This is not a problem of semantic similarity! Opposite to it, the problem is that completely different semantic cues might constitute pragmatically valid responses. Then, semantic similarity itself is not enough to evaluate a dialogue system response. Dialogue system response evaluation must go beyond semantics (This is actually what your M and N matrices are helping to do!!!)   “an accurate model that can evaluate dialogue response quality automatically — what could be considered an automatic Turing test —“ The original intention of Turing test was to be a proxy to identify/define intelligent behaviour. It actually proposes a test on intelligence based on an “intelligent” machine capability to imitate human behaviour in such a way that it would be difficult for a common human to distinguish between such a machine responses and actual human responses. It is of course related to dialogue system performance, but I think it is not correct to say that automatically evaluating dialogue response quality is an automatic Turing test. Actually, the title itself “Towards an Automatic Turing Test” is somehow misleading!  “the simplifying assumption that a ‘good’ chatbot is one whose responses are scored highly on appropriateness by human evaluators.” This is certainly the correct angle to introduce the problem of non-task-oriented dialogue systems, rather than “Turing Test”. Regarding this, there has been related work you might like to take a look at, as well as to make reference to, in the WOCHAT workshop series (see the shared task description and corresponding annotation guidelines).  In the discussion session: “and has has been used” -> “and it has been used”

Ex Output:
en


Ex Input:
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 Output:

es