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].

- Strengths: The idea to investigate the types of relations between lexical items is very interesting and challenging. The authors make a good argument why going beyond analogy testing makes sense.    - Weaknesses: The paper does not justify or otherwise contextualize the choice of clustering for evaluation, rather than using a classification task, despite the fact that classification tasks are more straightforward to evaluate. No attempt is being made to explain the overall level of the results. How well would humans do on this task (given only the words, no context)?  - General Discussion:  I have read the authors' response.
en

- La herramienta APARD - La temática asociada a requerimientos y fábrica de software es interesante  - No está detallado el caso práctico que considere el uso APARD con sus ventajas y limitaciones - La sección que explica la implementación de la solución APARD debe ser mejorada - Mejorar conclusiones - La presentación:  con errores ortográficos y de redacción, escrito en primera persona, figuras con letras muy pequeñas, color blanco de letras en figuras la hacen poco legible, referencias escritas de diferente forma, etc. - Se recomienda no utilizar Wikipedia como referencia para un artículo científico. - Falta autor en referencias 6, falta nombre de tema en referencia 13 - Referencia 7 está escrita con autor de manera diferente a las otras y con título antes de autor. - Cambiar “Palabras claves” por “Palabras clave”. - De acuerdo a formato, poner como mínimo 5 palabras clave. - Pone referencia en otro formato, ejemplo, Greenfield y Short(2003) - Hay mucho detalle de tecnologías y herramientas y productos detallados en la sección Especificación Tecnológica de APARD - ¿Qué significa IQNET[z]? - Sugiero poner en Introducción las secciones que componen el paper.
es

The paper looks at the problem of transferring a policy learned in a simulator to  a target real-world system.  The proposed approach considers using an ensemble of simulated source domains, along with adversarial training, to learn a robust policy that is able to generalize to several target domains.  Overall, the paper tackles an interesting problem, and provides a reasonable solution.  The notion of adversarial training used here does not seem the same as other recent literature (e.g. on GANs).  It would be useful to add more details on a few components, as discussed in the question/response round.  I also encourage including the results with alternative policy gradient subroutines, even if they don’t perform well (e.g. Reinforce), as well as results with and without the baseline on the value function. Such results are very useful to other researchers.

en