TASK DEFINITION: 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].
PROBLEM: Este trabajo ofrece un análisis comparativo del uso de tecnología cableada y tecnología programada.  El ámbito de "trabajo relacionado" presentado en el documento es inexistente. En este contexto, no es posible determinar si los criterios de comparación sugeridos en el trabajo son, efectivamente, los pertinentes para realizar una evaluación.  Los criterios de comparación de tecnologías son en algunos casos objetivos y en otros casos subjetivos. En este sentido, el trabajo no sugiere cómo enfrentar la valoración de cada criterio para finalmente efectuar una comparación (por ejemplo, en el ítem "seguridad" o en el ítem "programación").  Existen problemas gramaticales (por ejemplo, "alto tiempo). Adicionalmente, el trabajo está pobremente ordenado, dificultando la comprensión por el bajo esfuerzo en localizar el contenido de las tablas de una manera legible.  No se visualiza un aporte real de este trabajo. En numerosas oportunidades se cita a "Rexroth Bosch Group", sin agregar mucho más en cada punto. El impacto y relevancia  de este trabajo, en cuanto a la novedad, no es observable.

SOLUTION: es

PROBLEM: The reviewers seem to agree that the framework presented is not very novel, something I agree with.  The experiments show that the low rank + diagonal parameterization can be useful, however. The paper could be improved by making a more tightened message, and clearer arguments. As it currently stands, however it does not seem ready for publication in ICLR.

SOLUTION: en

PROBLEM: This paper proposes SEM, a simple large-size multilabel learning algorithm which models the probability of each label as softmax(sigmoid(W^T X) + b), so a one-layer hidden network. This in and of itself is not novel, nor is the idea of optimizing this by adagrad. Though it's weird that the paper explicitly derives the gradient and suggests doing alternating adagrad steps instead of the more standard adagrad steps; it's unclear whether this matters at all for performance. The main trick responsible for increasing the efficiency of this model is the candidate label sampling, which is done in a relatively standard way by sampling labels proportionally to their frequency in the dataset.  Given that neither the model nor the training strategy is novel, it's surprising that the results are better than the state-of-the-art in quality and efficiency (though non-asymptotic efficiency claims are always questionable since implementation effort trades off fairly well against performance). I feel like this paper doesn't quite meet the bar.

SOLUTION:
en