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: Se presenta el desarrollo de un prototipo para una herramienta de asignación automática de requisitos de software entre los desarrolladores. Esta herramienta puede ser muy útil para los equipos de desarrollo de software. Se trata de realizar un aporte mediante esta herramienta a  la calidad de los productos desarrollados.  No se explica cuál es la heurística utilizada para la asignación de requisitos a los programadores. No se presentan un estudio de las herramientas de gestión de requisitos existentes, en donde se demuestre que no existen herramientas alternativas a la presentada. Y en caso de existir alternativas, justificar por qué desarrollar una nueva. No se explica claramente las funcionalidades de la herramienta. No hay evidencia de la mejora del proceso al incluir esta herramienta, sólo se dice en las conclusiones que "es perceptible el cambio en la calidad del proceso" Las referencias bibliográficas utilizadas son en su mayoría páginas web, no se nota una revisión bibliográfica en bases de datos especializadas. Las figuras no se distinguen bien. Las referencias bibliográficas no cumplen el formato establecido. Debe redactar en tercera persona.

A: es
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Q: The idea of building a graph-based differentiable memory is very good. The proposed approach is quite complex, but it is likely to lead to future developments and extensions. The paper has been much improved since the original submission. The results could be strengthened, with more comparisons to existing results on bAbI and baselines on the experiments here. Exploring how it performs with less supervision, and different types of supervision, from entirely labeled graphs versus just node labels, would be valuable.

A: en
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Q: - Strengths: A well written paper, examining the use of context in lexical entailment task is a great idea, a well defined approach and experimental set-up and good analysis of the results   - Weaknesses: Some information is missing or insufficient, e.g., the table captions should be more descriptive, a clear description for each of the word type features should be given.  General Discussion:   The paper presents a proposal of consideration of context in lexical entailment task. The results from the experiments demonstrate that context-informed models do better than context-agnostic models on the entailment task.   I liked the idea of creating negative examples to get negative annotations automatically in the two ways described in the paper based on WordNet positive examples. (new dataset; an interesting method to develop dataset)  I also liked the idea of transforming already-used context-agnostic representations into contextualized representations, experimenting with different ways to get contextualized representations (i.e., mask vs contetx2vec), and testing the model on 3 different datasets (generalizability not just across different datasets but also cross-linguistically).  Motivations for various decisions in the experimental design were good to see, e.g., why authors used the split they used for CONTEXT-PPDB (it showed that they thought out clearly what exactly they were doing and why).  Lines 431-434: authors might want to state briefly how the class weights were determined and added to account for the unbalanced data in the CONTEXT-WN experiments. Would it affect direct comparisons with previous work, in what ways?   Change in Line 589: directionality 4 --> directionality, as in Table 4  Suggested change in Line 696-697: is-a hierarchy of WordNet --> "is-a" hierarchy of WordNet   For the sake of completeness, represent "mask" also in Figure 1.  I have read the author response.

A:
en
****