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: SYNOPSIS: The paper proposes a new neural network-based model for reading comprehension (reading a passage of text and answering questions based on the passage). It is similar in spirit to several other recent models, with the main exception that it is able to predict answers of different lengths, as opposed to single words/tokens/entities. The authors compare their model on the Stanford Question Answering Dataset (SQuAD), and show improvements over the baselines, while apparently lagging quite far behind the current state of the art reported on the SQuAD leaderboard. THOUGHTS: The main novelty of the method is to be able to identify phrases of different lengths as possible answers to the question. However, both approaches considered -- using a POS pattern trie tree to filter out word sequences with POS tags matching those of answers in the training set, and brute-force enumeration of all phrases up to length N -- seem somewhat orthogonal to the idea of "learning end-to-end " an answer chunk extraction model. Furthermore, as other reviews have pointed out, it seems that the linguistic features actually contribute a lot to the final accuracy (Table 3). One could argue that these are easy to obtain using standard taggers, but it takes away even more from the idea of an "end-to-end trained" system. The paper is generally well written, but there are several crucial sections in parts describing the model where it was really hard for me to follow the descriptions. In particular, the attention mechanism seems fairly standard to me in a seq2seq sense (i.e. there is nothing architecturally novel about it, as is for instance the case with the Gated Attentive Reader). I may be missing something, but even after the clarification round I still don't understand how it is novel compared to standard attention used in for instance seq2seq models. Finally, although the method is shown to outperform the baseline method reported in the original paper introducing the SQuAD dataset, it currently seems to be 12th (out of 15 systems) on the leaderboard (

SOLUTION: en

PROBLEM: El articulo describe una propuesta de arquitectura para sistemas abiertos en el contexto de agentes inteligentes. Aborda el aspecto teorico un determinado tipo de agentes en forma adecuada, dejando fuera el tipo de agentes recomendadores. La propuesta esta bien descrita, detallada, y formalizada, lo que deja de manifiesto un trabajo interesante para ser presentado. EL detalle va solamente por el lado de la evaluacion, quizas muy acotada y con poco desarrollo, considero que probar la arquitectura con un numero reducido (50) requerimientos es muy poco para tener una evaluacion empirica ad-hoc de su desempeno. En la forma, hay pequenos detalles ortograficos, puntuacion y la figura 1 no es referenciada en al articulo. Tambien hay titulos en ingles como el II b y el II a. Finalmente, llama la atencion la seccion de agradecimientos que esta en ingles y da a a entender que el trabajo esta inserto en un proyecto financiado por Conicyt.

SOLUTION: es

PROBLEM: Strengths -- An interesting proposal for a smaller CNN architecture designed for embedded CNN applications. -- Balanced exploration of CNN macroarchitecture and microarchitecture with fire modules. -- x50 less memory usage than AlexNet, keeping similar accuracy -- strong experimental results Weaknesses --Would be nice to test Sqeezenet on multiple tasks --lack of insights and rigorous analysis into what factors are responsible for the success of SqueezeNet. For example, how are ResNet and GoogleNet connected to the current architecture? Another old paper (Analysis of correlation structure for a neural predictive model with application to speech recognition, Neural Networks, 1994) also showed that the "by-pass" architecture by mixing linear and nonlinear prediction terms improves long term dependency in NN based on rigorous perturbation analysis. Can the current work be placed more rigorously on theoretical analysis?

SOLUTION:
en