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: El trabajo presenta el desarrollo un web framework para la transmisión con capacidad de análisis de imágenes de cámaras IP.  La bibliografía presentada son páginas webs relacionadas a las herramientas utilizadas para el desarrollo, pero no a temas relacionados  u otras aplicaciones relacionadas.  Problemas de formato (revisar ortografía,  saltos de línea cuando no debería, identación,  Indica en el resumen que el sistema tiene capacidad de análisis, pero al final indica que está desactivada.  ¿Cuál es la ventaja del ahorro en uso de Ips reales?  Falta un análisis de la situación anterior con la actual. En qué mejoró el sistema.

SOLUTION: es

PROBLEM: This paper presents an approach to learn to generate programs. Instead of directly trying to generate the program, the authors propose to train a neural net to estimate a fix set of attributes, which then condition a search procedure. This is an interesting approach, which make sense, as building a generative model of programs is a very complex task.  Faster computation times are shown in the experimental section with respect to baselines including DFS, Enumeration, etc. in a setup with very small programs of length up to 5 instructions have to be found.  It is not clear to me how the proposed approach scales to larger programs, where perhaps many attributes will be on. Is there still an advantage?  The authors use as metric the time to find a single program, whose execution will result in the set of 5 input-output pairs given as input. However, as mentioned in the paper, one is not after a generic program but after the best program, or a rank list of all programs (or top-k programs) that result in a correct execution. Could the authors show experiments in this setting? would still be useful to have the proposed approach? what would the challenges be in this more realistic scenario?  In the second experiment the authors show results where the length of the program at training time is different than the length at test time. However, the results are shown when only 20% of the programs are finished. Could you show results for finding all programs?   The paper is missing an analysis of the results. What type of programs are difficult? how often is the NNet wrong? how does this affect speed? what are the failure modes of the proposed method?  The authors proposed to have a fix-length representation of the each input-output pair, and then use average pooling to get the final representation. However, why would average pooling make sense here? would it make more sense to combine the predictions at the decoder, not the encoder?  Learning from only 5 executions seems very difficult to me. For programs so small it might be ok, but going to more difficult and longer programs this setting does not seem reasonable.   In summary an interesting paper. This paper tackles a problem that is outside my area of expertise so I might have miss something important.

SOLUTION: en

PROBLEM: In this paper, the authors propose a Bayesian variant of the skipgram model to learn word embeddings. There are two important variant compared to the original model. First, aligned sentences from multiple languages are used to train the model. Therefore, the context words of a given target word can be either from the same sentence, or from an aligned sentence in a different language. This allows to learn multilingual embedding. The second difference is that each word is represented by multiple vectors, one for each of its different senses. A latent variable z models which sense should be used, given the context.  Overall, I believe that the idea of using a probabilistic model to capture polysemy is an interesting idea. The model introduced in this paper is a nice generalization of the skipgram model in that direction. However, I found the paper a bit hard to follow. The formulation might probably be simplified (e.g. why not consider a target word w and a context c, where c is either in the source or target language. Since all factors are independent, this should not change the model much, and would make the presentation easier). The performance of all models reported in Table 2 & 3 seem pretty low.  Overall, I like the main idea of the paper, which is to represent word senses by latent variables in a probabilistic model. I feel that the method could be presented more clearly, which would make the paper much stronger. I also have some concerns regarding the experimental results.  Pros: Interesting extension of skipgram to capture polysemy. Cons: The paper is not clearly written. Results reported in the paper seems pretty low.

SOLUTION:
en