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]: The paper proposes an approach to sequence transduction for the case when a monotonic alignment between the input and the output is plausible. It is assumed that the alignment can be provided as a part of training data, with Chinese Restaurant process being used in the actual experiments.   The idea makes sense, although its applicability is limited to the domains where a monotonic alignment is available. But as discussed during the pre-review period, there has been a lot of strongly overlapping related work, such as probabilistic models with hard-alignment (Sequence Transduction With Recurrent Neural Network, Graves et al, 2012) and also attempts to use external alignments in end-to-end models (A Neural Transducer, Jaitly et al, 2015). That said, I do not think the approach is sufficiently novel.   I also have a concern regarding the evaluation. I do not think it is fair to compare the proposed model that depends on external alignment with the vanilla soft-attention model that learns alignments from scratch. In a control experiment soft-attention could be trained to match the external alignment. Such a pretraining could reduce overfitting on the small dataset, the one on which the proposed approach brings the most improvement. On a larger dataset, especially SIGMORPHON, the improvements are not very big and are only obtained for a certain class of languages.  To sum up, two main issues are (a) lack of novelty (b) the comparison of a model trained with external alignment and one without it.
[EX A]: en

[EX Q]: - Strengths: A nice, solid piece of work that builds on previous studies in a productive way. Well-written and clear.   - Weaknesses:   Very few--possibly avoid some relatively "empty" statements:  191 : For example, if our task is to identify words used similarly across contexts, our scoring function can be specified to give high scores to terms whose usage is similar across the contexts.  537 : It is educational to study how annotations drawn from the same data are similar or different.  - General Discussion: In the first sections I was not sure that much was being done that was new or interesting, as the methods seemed very reminiscent of previous methods used over the past 25 years to measure similarity, albeit with a few new statistical twists, but conceptually in the same vein. Section 5, however, describes an interesting and valuable piece of work that will be useful for future studies on the topic. In retrospect, the background provided in sections 2-4 is useful, if not necessary, to support the experiments in section 5.   In short, the work and results described will be useful to others working in this area, and the paper is worthy of presentation at ACL.  Minor comments:  Word, punctuation missing? 264 : For word annotations, we used PPMI, SVD, and SGNS (skipgram with negative sampling from Mikolov et al. (2013b)) word vectors released by Hamilton et al. (2016).  Unclear what "multiple methods" refers to : 278 : some words were detected by multiple methods with CCLA
[EX A]: en

[EX Q]: El paper está bien escrito y de fácil lectura.  Se ajusta al formato del evento.  Estado del arte bien planteado y revisado.  El rechazo débil es por qué no está de acuerdo a los tópicos de interés de los trabajos de INFONOR, ver listado en [Link]#areas-interes  El paper debería ser presentado en un evento de Educación en Ingeniería. Para los cual se sugiere que los resultados obtenidos sean comparables con asignaturas sin tutorías y analizar los resultados de aprendizajes que logran los alumnos en grupos de control y experimental.
[EX A]:
es