instruction:
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].
question:
The paper presents a method to learn graph embeddings in a unsupervised way using random walks. It is well written and the execution appears quite accurate. The area of learning whole graph representations does not seem to be very well explored in general, and the proposed approach enjoys having very few competitors. In a nutshell, the idea is to linearize the graph using random walks and to compute the embedding of the central segment of each walk using the skip-thought criterion. Being not an expert in biology, I can not comment whether or not this makes sense, but the gains reported in Table 2 are quite significant. An anonymous public comment compared this work to a number of others in which the problem of learning representations of nodes is considered. While this is arguably a different goal, one natural baseline would be to pool these representations using mean- or max- pooling. It would very interesting to do such a comparison, especially given that the considered approach heavily relies on pooling (see Figure 3(c)) To sum up, I think it is a nice paper, and with more baselines I would be ready to further increase the numerical score.
answer:
en

question:
Thanks for the response. I look forward to reading about the effect of incentives and the ambiguity of the language in the domain. Review before author response: The paper proposes a way to build natural language interfaces by allowing a set of users to define new concepts and syntax. It's an (non-trivial) extension of S. I. Wang, P. Liang, and C. Manning. 2016. Learning language games through interaction Questions: - What is the size of the vocabulary used - Is it possible to position this paper with respect to previous work on inverse reinforcement learning and imitation learning ? Strengths: - The paper is well written - It provides a compelling direction/solution to the problem of dealing with a large set of possible programs while learning natural language interfaces. Weaknesses: - The authors should discuss the effect of the incentives on the final performance ? Were other alternatives considered ? - While the paper claims that the method can be extended to more practical domains, it is not clear to me how straightforward it is going to be. How sensitive is the method to the size of the vocabulary required in a domain ? Would increased ambiguity in natural language create new problems ? These questions are not discussed in the current experiments. - A real-world application would definitely strengthen the paper even more.
answer:
en

question:
RESUMEN: El trabajo relata los pasos seguidos para usar el software ERP SAP en Embonor, una productora y distribuidora de productos Coca-cola. Antes de reportar esta experiencia, esta empresa ya contaba con algunos modulos SAP implementados (Financieros, FI-CO, Logistica, Recursos Humanos). Evaluacion General. Aunque el trabajo no presenta una estructura cientifica de un articulo, es sumamente interesante (en mi opinion) escuchar en mas detalle de esta experiencia en infonor 2013. Tal vez, recomendaria agregar/presentar un poco mas acerca de las evaluaciones esta implantacion (en su conclusion, mencionan esto como un trabajo futuro). Comentarios menores: - Principalmente cuidar los espaciados entre palabras y puntos. - Figura 1 es muy poco nitida. - Figura 4 dice: "Menus de la tabla xx (estandar en cada pantalla SAP)" xx?
answer:
es