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]: This paper gives a theoretical motivation for tieing the word embedding and output projection matrices in RNN LMs. The argument uses an augmented loss function which spreads the output probability mass among words with close word-embedding. I see two main drawbacks from this framework: The augmented loss function has no trainable parameters and is used for only for regularization. This is not expected to give gains with large enough datasets. The augmented loss is heavily "engineered" to produce the desired result of parameter tying. It's not clear what happens if you try to relax it a bit, by adding parameters, or estimating y~ in a different way. Nevertheless the argument is very interesting, and clearly written. The simulated results indeed validate the argument, and the PTB results seem promising. Minor comments: Section 3: Can you clarify if y~ is conditioned on the t example or on the entire history. Eq. 3.5: i is enumerated over V (not |V|)
[A]: en

[Q]: Taking into account the loss in the binarization step through a proximal Newton algorithm is a nice idea. This is at least one approach to bringing in the missing loss in the binarization step, which has recently gone from a two step process of train and binarize to a single step simultaneous train/compress. Performance on a few small tasks show the benefit. It would be nice to see some results on substantial networks and tasks which really need compression on embedded systems (a point made in the introduction). Is it necessary to discuss exploding/vanishing gradients when the RNN experiments are carried out by an LSTM, and handled by the cell error carousel? We see the desire to tie into proposition 2, but not clear that the degradation we see in the binary connect is related. Adam is used in the LSTM optimization, was gradient clipping really needed, or is the degradation of binary connect simply related to capacity? For proposition 3.1, theorem 3.1 and proposition 3.2 put the pointers to proofs in appendix.
[A]: en

[Q]: El documento describe un trabajo exploratorio en el desarrollo de una aplicacion software en el area de informatica medica, concretamente en los sistemas HIS (Hospital Information System). La propuesta es interesante pero el documento denota que es un trabajo insipiente, o se entiende esto, dado que se describe la arquitectura propuesta y la metodologia del proyecto, pero no se describe un escenario de prueba o un caso concreto de validacion. Igualmente, no se observa en el documento la descripcion de un software resultado del proyecto, y por el titulo del articulo y el abstract se espera la descripcion de alguna propuesta software.
[A]:
es