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].

[Q]: - Strengths: The paper presents an interesting extension to attention-based neural MT approaches, which leverages source-sentence chunking as additional piece of information from the source sentence. The model is modified such that this chunking information is used differently by two recurrent layers: while one focuses in generating a chunk at a time, the other focuses on generating the words within the chunk. This is interesting. I believe readers will enjoy getting to know this approach and how it performs. The paper is very clearly written, and alternative approaches are clearly contrasted. The evaluation is well conducted, has a direct contrast with other papers (and evaluation tables), and even though it could be strengthened (see my comments below), it is convincing.  - Weaknesses: As always, more could be done in the experiments section to strengthen the case for chunk-based models. For example, Table 3 indicates good results for Model 2 and Model 3 compared to previous papers, but a careful reader will wonder whether these improvements come from switching from LSTMs to GRUs. In other words, it would be good to see the GRU tree-to-sequence result to verify that the chunk-based approach is still best.  Another important aspect is the lack of ensembling results. The authors put a lot of emphasis is claiming that this is the best single NMT model ever published. While this is probably true, in the end the best WAT system for Eng-Jap is at 38.20 (if I'm reading the table correctly) - it's an ensemble of 3. If the authors were able to report that their 3-way chunk-based ensemble comes top of the table, then this paper could have a much stronger impact.  Finally, Table 3 would be more interesting if it included decoding times. The authors mention briefly that the character-based model is less time-consuming (presumably based on Eriguchi et al.'16), but no cite is provided, and no numbers from chunk-based decoding are reported either. Is the chunk-based model faster or slower than word-based? Similar? Who know... Adding a column to Table 3 with decoding times would give more value to the paper.  - General Discussion: Overall I think the paper is interesting and worth publishing. I have minor comments and suggestions to the authors about how to improve their presentation (in my opinion, of course).   * I think they should clearly state early on that the chunks are supplied externally - in other words, that the model does not learn how to chunk. This only became apparent to me when reading about CaboCha on page 6 - I don't think it's mentioned earlier, and it is important.  * I don't see why the authors contrast against the char-based baseline so often in the text (at least a couple of times they boast a +4.68 BLEU gain). I don't think readers are bothered... Readers are interested in gains over the best baseline.  * It would be good to add a bit more detail about the way UNKs are being handled by the neural decoder, or at least add a citation to the dictionary-based replacement strategy being used here.  * The sentence in line 212 ("We train a GRU that encodes a source sentence into a single vector") is not strictly correct. The correct way would be to say that you do a bidirectional encoder that encodes the source sentence into a set of vectors... at least, that's what I see in Figure 2.  * The motivating example of lines 69-87 is a bit weird. Does "you" depend on "bite"? Or does it depend on the source side? Because if it doesn't depend on "bite", then the argument that this is a long-dependency problem doesn't really apply.
[A]: en


[Q]: El artículo presenta el desarrollo de una aplicación móvil, no incluye ningún elemento de investigación asociada a dicho desarrollo. Simplemente se centra en la aplicación del proceso RUP para el desarrollo. No se referencian los textos o artículos utilizados para definiciones o aseveraciones realizadas, por ejemplo, sobre RUP "....es enfocada hacia "diagramas de los casos de uso, y manejo de los riesgos y el manejo de la arquitectura" como tal "   Si está entre cremillas, supongo será una copia textual de alguna parte que no se indica No se presenta un análisis profundo de la estrategia de solución ¿cuál es la lógica detrás de la aplicación móvil? Se observan algunos problemas en la redacción en el texto, por ejemplo, "...su objetivo es asegurar la producción de software de alta y mayor calidad...." La estructura del artículo tiene algunas deficiencias La figura 1 no aporta información al trabajo realizado, no es necesaria. La figura 2 no se distingue bien, además está tomada del libro de UML de Larman y no aparece la referencia en el texto. Las conclusiones del trabajo son extremadamente básicas
[A]: es


[Q]: The paper presents an investigation of various neural language models designed to query context information from their recent history using an attention mechanism. The authors propose to separate the attended vectors into key, value and prediction parts. The results suggest that this helps performance. The authors also found that a simple model which which concatenates recent activation vectors performs at a similar level as the more complicated attention-based models.  The experimental methodology seems sound in general. I do have some issues with the way the dimensionality of the vectors involved in the attention-mechanism is chosen. While it’s good that the hidden layer sizes are adapted to ensure similar numbers of trainable parameters for all the models, this doesn’t control for the fact that key/value/prediction vectors of a higher dimensionality may simply work better regardless of whether their dimensions are dedicated to one particular task or used together. This separation clearly saves parameters but there could also be benefits of having some overlap of information assuming that vectors that lead to similar predictions may also be required in similar contexts for example. Some tasks may also require more dimensions than others and the explicit separation prevents the model from discovering and exploiting this.   While memory augmented RNNs and RNNs with attention mechanisms are not new, some of these architectures had not yet been applied to language modeling. Similarly (and as acknowledged by the authors), the strategy of separating key and value functionality has been proposed before, but not in the context of natural language modeling. I’m not sure about the novelty of the proposed n-gram RNN because I recall seeing similar architectures before but I understand that novelty was not the point of that architecture as it mainly serves as a proof of the lack of ability of the more complicated architectures to do better. In that sense I do consider it an inventive baseline that could be used in future work to test the ability of other models that claim to exploit long-term dependencies.   The exact computation of the representation h_t was initially not that clear to me (the terms hidden and output can be ambiguous at times) but besides this, the paper is quite clear and generally well-written.  The results in this paper are important because they show that learning long-term dependencies is not a solved problem by any means. The authors provide a very nice comparison to prior results and the fact that their n-gram RNN is often at least competitive with far more complicated approaches is a clear indication that some of those methods may not capture as much context information as previously thought. The success of the separation of key/value/prediction functionality in attention-based system is also noteworthy although I think this is something that needs to be investigated more thoroughly (i.e., with more control for hyperparameter choices).    Pros: Impressive and also interesting results. Good comparison with earlier work. The n-gram RNN is an interesting baseline.   Cons: The relation between the attention-mechanism type and the number of hidden units weakens the claim that the key/value/prediction separation is the reason for the increase in performance somewhat. The model descriptions are not entirely clear. I would have liked to have seen what happens when the attention is applied to a much larger context size.
[A]:
en