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].

La novedad del tema a tratar hace del artículo una investigación interesante. Tiene una muy buena presentación y es fácil de leer, lo cual da un aspecto llamativo al tema. A pesar de ser un tema que incluye dos disciplinas muy distintas, han logrado sincronizar adecuadamente los conceptos de tal forma que la investigación es importante y aporta conocimiento desde la parte inicial del trabajo realizado, hasta las propias conclusiones Se recomienda a los autores seguir con el trabajo a fin de acabar otros aspectos relacionados con la salud pública.    Recomendaciones: Es indispensable mejorar la calidad de las figuras, existen algunas que es imposible visualizar, como es el caso de Figura 4, Figura 6, Figura 7 y Figura 8.
es

- Strengths: The idea of hard monotonic attention is new and substantially different from others.  - Weaknesses: The experiment results on morphological inflection generation is somewhat mixed. The proposed model is effective if the amount of training data is small (such as CELEX). It is also effective if the alignment is mostly monotonic and less context sensitive (such as Russian, German and Spanish).  - General Discussion:  The authors proposed a novel neural model for morphological inflection generation which uses "hard attention", character alignments separately obtained by using a Bayesian method for transliteration. It is substantially different from the previous state of the art neural model for the task which uses "soft attention", where character alignment and conversion are solved jointly in the probabilistic model.  The idea is novel and sound. The paper is clearly written. The experiment is comprehensive. The only concern is that the proposed method is not necessarily the state of the art in all conditions. It is suitable for the task with mostly monotonic alignment and with less context sensitive phenomena. The paper would be more convincing if it describe the practical merits of the proposed method, such as the ease of implementation and computational cost.
en

The authors propose and evaluate using SPN's to generate embeddings of input and output variables, and using MPN to decode output embeddings to output variables. The advantage of predicting label embeddings is to decouple dependencies in the predicted space. The authors show experimentally that using SPN based embeddings is better than those produced by RBM's.  This paper is fairly dense and a bit hard to read. After the discussion, the main contributions of the authors are:  1. They propose the scheme of learning SPN's over Y and then using MPN's to decode the output, or just SPNs to embed X. 2. They propose how to decode MPN's with partial data. 3. They perform some analysis of when their scheme will lead to perfect encoding/decodings. 4. They run many, many experiments comparing various ways of using their proposed method to make predictions on multi-label classification datasets.  My main concerns with this paper are as follows:  - The point of this paper is about using generative models for representation learning. In their experiments, the main task is discriminative; e.g. predict multiple Y from X. The only discriminative baseline is a L2 regularized logistic regression, which does not have any structure on the output; it'd be nice to see how a discriminative structured prediction method would do, such as CRF or belief propagation.   - The many experiments suggest that their encoder/decoder scheme is working better than the alternatives; can you please give more details on the relative computation complexity of each method?  - One thing I'm still having trouble understanding is *why* this method works better than MADE and the other alternatives. Is it learning a better model of the distribution of Y? Is it better at separating out correlations in the output into individual nodes?  Does it have larger representations?   - I think the experiments are overkill and if anything, they way they are presented detract from the paper. There's already far too many numbers and graphs presented to be easy to understand.  If I have to dig through hundreds of numbers to figure out if your claim is correct, the paper is not clear enough. And, I said this before in my comments, please do not refer to Q1, Q2, etc. -- these shortcuts let you make the paper more dense with fewer words but at the cost of readability.  I *think* I convinced myself that your method works...I would love to see a table that shows, for each condition: (A) a baseline X->Y, (B) one *average* result across datasets for your method, and (C) one *average* result from a reasonable best competitor method. Please show for both the exact match and hamming losses, as that will demonstrate the gap between independent linear prediction and structured prediction. That would still be plenty of numbers but would make it much easier for the reader to verify your claims and you can put everything else in the Appendix.  E.g. something like:  Input | Predicted Output | Decoder | Hamming | Exact Match ---- X | P(Y) | CRF | xx.xx | xx.xx   (this is your baseline) SPN E_X | P(Y) | n/a | xx.xx | xx.xx  X | SPN E_Y | MPN | xx.xx | xx.xx  (given X, predict E_Y, then decode it with an MPN)  Does a presentation like that make sense? It's just really hard and time-consuming for me as a reviewer to verify your results, the way you've laid them out currently.

en