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].

[EX Q]: 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.
[EX A]: en

[EX Q]: First, I'd like to thank the authors for their answers and clarifications. I find, the presentation of the multi-stage version of the model much clearer now.  Pros:  + The paper states a sparse coding problem using cosine loss, which allows to solve the problem in a single pass.  + The energy-based formulation allows bi-directional coding that incorporates top-down and bottom-up information in the feature extraction process.   Cons:  + The cost of running the evaluation could be large in the  multi-class setting, rendering the approach less attractive and the computational cost comparable to recurrent architectures.  + While the model is competitive and improves over the baseline, the paper would be more convincing with other comparisons (see text). The experimental evaluation is limited (a single database and a single baseline)  ------  The motivation of the sparse coding scheme is to perform inference in a feed forward manner. This property does not hold in the multi stage setting, thus optimization would be required (as clarified by the authors).  Having an efficient way of performing a bi-directional coding scheme is very interesting. As the authors clarified, this could not necessarily be the case, as the model needs to be evaluated many times for performing a classification.  Maybe an interesting combination would be to run the model without any class-specific bias, and evaluation only the top K predictions with the energy-based setting.  Having said this, it would be good to include a discussion (if not direct comparisons) of the trade-offs of using a model as the one proposed by Cao et al. Eg. computational costs, performance.  Using the bidirectional coding only on the top layers seems reasonable: one can get a good low level representation in a class agnostic way. This, however could be studied in more detail, for instance showing empirically the trade offs. If I understand correctly, now only one setting is being reported.  Finally, the authors mention that one benefit of using the architecture derived from the proposed coding method is the spherical normalization scheme, which can lead to smoother optimization dynamics. Does the baseline (or model) use batch-normalization? If not, seems relevant to test.   Minor comments:  I find figure 2 (d) confusing. I would not plot this setting as it does not lead to a function (as the authors state in the text).
[EX A]: en

[EX Q]: Extended the paper with experiments on the word relationship dataset, showing Doc2VecC generates better word embeddings in comparison to Word2Vec or Paragraph Vectors. 
[EX A]:
en