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: Trabajo muy mal presentado, con severos errores de ortografía y redacción. Pese al tema, interesante, no es posible encontrar un hilo adecuado en el desarrollo del trabajo.  Desde el inicio surgen los problemas, en el resumen se afirma que "el objetivo de este artículo es la realización de un análisis a un conjunto de registros de diagnósticos médicos, puestos a manera de investigación, en busca de una mejor perspectiva y visualización de los datos para responder a los indicadores que se plantearon" Primero, el objetivo del artículo no es la realización del análisis, sino mostrar el resultado de algún proceso (el proceso puede ser el análisis).La frase "puestos a manera de investigación" es del todo inadecuada.  No se responde a los indicadores..., ¿qué sentido tiene eso?  Las citas en itálico incluyen a los autores; faltó apoyo de alguien más experimentado en la escritura del artículo.  Lamentablemente, el trabajo está plagado de errores inaceptables.  No sólo de escritura.  La página 3 indica que uno de los problemas encontrados dice relación con la "Estructuración de datos". ¿Cuál es el problema? ¿Por qué se produce? ¿Cómo lo enfrenta el autor? ¿Es satisfactorio su enfoque?  Creo que esos antecedentes (que se repiten de manera similar en toda la extensión del texto), justifican mi rechazo.  Resumiendo, es muy pobre el trabajo.

Output: es


Input: Consider Input: This work combines a LDA-type topic model with a RNN and models this by having an additive effect on the predictive distribution via the topic parameters. A variational auto-encoder is used to infer the topic distribution for a given piece of text and the RNN is trained as a RNNLM. The last hidden state of the RNNLM and the topic parameters are then concatenated to use as a feature representation.  The paper is well written and easy to understand. Using the topic as an additive effect on the vocabulary allows for easy inference but intuitively I would expect the topic to affect the dynamics too, e.g. the state of the RNN. The results on using this model as a feature extractor for IMDB are quite strong. Is the RNN fine-tuned on the labelled IMDB data? However, the results for PTB are weaker. From the original paper, an ensemble of 2 LSTMs is able to match the topicRNN score. This method of jointly modelling topics and a language model seems effective and relatively easy to implement.  Finally, the IMDB result is no longer state of the art since this result appeared in May (Miyato et al., Adversarial Training Methods for Semi-Supervised Text Classification).  Some questions: How important is the stop word modelling? What do the results look like if l_t = 0.5 for all t?  It seems surprising that the RNN was more effective than the LSTM. Was gradient clipping tried in the topicLSTM case? Do GRUs also fail to work?  It is also unfortunate that the model requires a stop-word list. Is the link in footnote 4 the one that is used in the experiments?  Does factoring out the topics in this way allow the RNN to scale better with more neurons? How reasonable does the topic distribution look for individual documents? How peaked do they tend to be? Can you show some examples of the inferred distribution? The topics look odd for IMDB with the top word of two of the topics being the same: 'campbell'. It would be interesting to compare these topics with those inferred by LDA on the same datasets.  Minor comments: Below figure 2: GHz -> GB \Gamma is not defined.

Output: en


Input: Consider Input: - Strengths:  This is the first neural network-based approach to argumentation mining. The proposed method used a Pointer Network (PN) model with multi-task learning and outperformed previous methods in the experiments on two datasets.  - Weaknesses:  This is basically an application of PN to argumentation mining. Although the combination of PN and multi-task learning for this task is novel, its novelty is not enough for ACL long publication. The lack of qualitative analysis and error analysis is also a major concern.  - General Discussion:  Besides the weaknesses mentioned above, the use of PN is not well-motivated. Although three characteristics of PN were described in l.138-143, these are not a strong motivation against the use of bi-directional LSTMs and the attention mechanism. The authors should describe what problems are solved by PN and discuss in the experiments how much these problems are solved.  Figures 2 and 3 are difficult to understand. What are the self link to D1 and the links from D2 to E1 and D3/D4 to E2? These are just the outputs from the decoder and not links. The decoder LSTM does not have an input from e_j in these figures, but it does in Equation (3). Also, in Figure 3, the abbreviation "FC" is not defined.  Equation (8) is strange. To calculate the probability of each component type, the probability of E_i is calculated.  In the experiments, I did not understand why only "PN", which is not a joint model, was performed for the microtext corpus.  It is not clear whether the BLSTM model is trained with the joint-task objective.  There are some studies on discourse parsing using the attention mechanism. The authors should describe the differences from these studies.  Minor issues:  l.128: should related -> should be related  l.215: (2015) is floating  l.706: it able -> it is able  I raised my recommendation score after reading the convincing author responses. I strongly recommend that the authors should discuss improved examples by PN as well as the details of feature ablation.


Output: en