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: - 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.

Output: en


Input: Consider Input: El trabajo, según los revisores, no presenta una contribución científica directa en el área de robótica o sistemas difusos, sin embargo es una aplicación muy interesante a ser presentada en el Workshop de Robótica de INFONOR 2013. Es por ello que se recomienda aceptar el artículo para su presentación.

Output: es


Input: Consider Input: This paper essentially presents a new inductive bias in the architecture of (convolutional) neural networks (CNN). The mathematical motivations/derivations of the proposed architecture are detailed and rigorous. The proposed architecture promises to produce equivariant representations with steerable features using fewer parameters than traditional CNNs, which is particularly useful in small data regimes. Interesting and novel connections are presented between steerable filters and so called “steerable fibers”. The architecture is strongly inspired by the author’s previous work, as well as that of “capsules” (Hinton, 2011). The proposed architecture is compared on CIFAR10 against state-of-the-art inspired architectures (ResNets), and is shown to be superior particularly in the small data regime. The lack of empirical comparison on large scale dataset, such as ImageNet or COCO makes this largely a theoretical contribution. I would have also liked to see more empirical evaluation of the equivariance properties. It is not intuitively clear exactly why this architecture performs better on CIFAR10 as it is not clear that capturing equivariances helps to classify different instances of object categories. Wouldn’t action-recognition in videos, for example, not be a better illustrative dataset?


Output: en