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].

Example 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”
Example Output: en

Example Input: -	El tema de adopción de sistemas ERP se ha desarrollado los últimos 15 años, y en particular la revisión de factores que impliquen su éxito es un tema de investigación relevante. -	En el trabajo se desarrolló un análisis de la literatura ERP sobre el tema que aborda   factores críticos de éxito, metodologías de adopción y tamaño de la organización. -	Si bien es un error indicar que "últimamente se  ha generado un tercer tipo..." en relación a asociar fases del proyecto y FCE, pues hace más de 12 años esto se ha realizado, ver Cita [36] del mismo trabajo, no es un tema cerrado. -	Propongo a los autores el definir que es "éxito de adopción ERP", pues existe literatura en relación a ello muy interesante para sus fines últimos (ver trabajos de la profesora Markus). -	Metodológicamente existen tres limitaciones importantes a superar con miras a avanzar en una publicación científica de impacto. Primero, no se ha considerado el factor de impacto de la publicaciones, con lo cual se ha desestimado el desarrollo asociado a este tipo de revisiones, mezclando trabajos de alto impacto (ejemplo, artículos de revistas ISIs factor de impacto >2, como MISQ) con trabajos sin una revisión de calidad (ejemplo, lo que Scholar "lanza" sólo por coincidir en la búsqueda). Segundo, se dice que se tomarán referencias del 2008 en adelante prioritariamente, pero muchas de sus referencias  son de antes de esa fecha, y eso no es raro, pues entre 2001 y 2004 hay una explosión de trabajos en este ámbito específico. Tercero, no se han incluido explícitamente trabajos de conferencias del área de alto impacto en la comunidad científica como AMCIS o ECIS (la primera tiene una base de datos privada, no accesible por google y la segunda es libre). -	Las limitaciones antes expuestas restringen los alcances de los interesantes  resultados expuestos en el trabajo.
Example Output: es

Example Input: This is a nice paper on morphological segmentation utilizing word  embeddings. The paper presents a system which uses word embeddings to  both measure local semantic similarity of word pairs with a potential  morphological relation, and global information about the semantic validity of potential morphological segment types. The paper is well written and  represents a nice extension to earlier approaches on semantically driven  morphological segmentation.  The authors present experiments on Morpho Challenge data for three  languages: English, Turkish and Finnish. These languages exhibit varying  degrees of morphological complexity. All systems are trained on Wikipedia  text.   The authors show that the proposed MORSE system delivers clear  improvements w.r.t. F1-score for English and Turkish compared to the well  known Morfessor system which was used as baseline. The system fails to  reach the performance of Morfessor for Finnish. As the authors note, this  is probably a result of the richness of Finnish morphology which leads to  data sparsity and, therefore, reduced quality of word embeddings. To  improve the performance for Finnish and other languages with a similar  degree of morphological complexity, the authors could consider word  embeddings which take into account sub-word information. For example,  @article{DBLP:journals/corr/CaoR16,   author    = {Kris Cao and                Marek Rei},   title     = {A Joint Model for Word Embedding and Word Morphology},   journal   = {CoRR},   volume    = {abs/1606.02601},   year                  = {2016},   url                 = {[Link]},   timestamp = {Fri, 01 Jul 2016 17:39:49 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  @article{DBLP:journals/corr/BojanowskiGJM16,   author    = {Piotr Bojanowski and                Edouard Grave and                Armand Joulin and                Tomas Mikolov},   title     = {Enriching Word Vectors with Subword Information},   journal   = {CoRR},   volume    = {abs/1607.04606},   year                  = {2016},   url                 = {[Link]},   timestamp = {Tue, 02 Aug 2016 12:59:27 +0200},   biburl    = {[Link]},   bibsource = {dblp computer science bibliography, [Link]} }  The authors critique the existing Morpho Challenge data sets.  For example, there are many instances of incorrectly segmented words in  the material. Moreover, the authors note that, while some segmentations  in the the data set may be historically valid (for example the  segmentation of business into busi-ness), these segmentations are no  longer semantically motivated. The authors provide a new data set  consisting of 2000 semantically motivated segmentation of English word  forms from the English Wikipedia. They show that MORSE deliver highly  substantial improvements compared to Morfessor on this data set.  In conclusion, I think this is a well written paper which presents  competitive results on the interesting task of semantically driven  morphological segmentation. The authors accompany the submission with  code and a new data set which definitely add to the value of the  submission.
Example Output:
en