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].

[EX Q]: Conclusiones: "... cambiar el paradigma del actual desarrollo de aplicaciones enfocadas en esta área, la pregunta a realizar ya no debe ser ¿Cómo se realizará el plegado de la proteína?, sino el cómo se puede mejorar la forma de aplicar las reglas de Meckler. ..."  LAS CONCLUSIONES NO SON CLARAS. HAY MUCHO TRABAJO A FUTURO. POR EJEMPLO: "...queda abierta la interrogante acerca de cómo se puede mejorar la simulación de tal manera que la secuencia de aminoácidos..."  DEBEN REDACTAR LAS CONCLUSIONES BASADAS EN LOS RESULTADOS EXPERIMENTALES Y LA TEORÍA USADA.
[EX A]: es

[EX Q]: this proposes a multi-view learning approach for learning representations for acoustic sequences. they investigate the use of bidirectional LSTM with contrastive losses. experiments show improvement over the previous work.  although I have no expertise in speech processing, I am in favor of accepting this paper because of following contributions: - investigating the use of fairly known architecture on a new domain. - providing novel objectives specific to the domain - setting up new benchmarks designed for evaluating multi-view models  I hope authors open-source their implementation so that people can replicate results, compare their work, and improve on this work.
[EX A]: en

[EX Q]: - Strengths: The paper proposes an end-to-end neural model for semantic graph parsing, based on a well-designed transition system.  The work is interesting, learning semantic representations of DMRS, which is capable of resolving semantics such as scope underspecification. This work shows a new scheme for computational semantics, benefiting from an end-to-end transition-based incremental framework, which resolves the parsing with low cost.  - Weaknesses:   My major concern is that the paper only gives a very common introduction for the definition of DMRS and EP, and the example even makes me a little confused because I cannot see anything special for DMRS. The description can be a little more detailed, I think. However, upon the space limitation, it is understandable. The same problem exists for the transition system of the parsing model. If I do not have any background of MRS and EP, I can hardly learn something from the paper, just seeing that this paper is very good.  - General Discussion:   Overall, this paper is very interesting to me. I like the DMRS for semantic parsing very much and like the paper very much. Hope that the open-source codes and datasets can make this line of research being a hot topic.
[EX A]:
en