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]: The paper proposes a model that aims at learning to label nodes of graph in a semi-supervised setting. The idea of the model is based on the use of the graph structure to regularize the representations learned at the node levels. Experimental results are provided on different tasks  The underlying idea of this paper (graph regularization) has been already explored in different papers – e.g 'Learning latent representations of nodes for classifying in heterogeneous social networks' [Jacob et al. 2014],   [Weston et al 2012] where a real graph structure is used instead of a built one. The experiments lack of strong comparisons with other graph models (e.g Iterative Classification, 'Learning from labeled and unlabeled data on a directed graph', ...). So the novelty of the paper and the experimental protocol are not strong enough to accpet the paper.  Pros: * Learning over graph is an important topic  Cons: * Many existing approaches have already exploited the same types of ideas, resulting in very close models * Lack of comparison w.r.t existing models
[A]: en


[Q]: Summary: The paper proposes a neural model for predicting Python syntax trees from text descriptions. Guided by the actual Python grammar, the model generates tree nodes sequentially in a depth-first fashion. Key ideas include injecting the information from the parent node as part of the LSTM input, a pointer network for copying the terminals, and unary closure which collapses chains of unary productions to reduce the tree size. The model is evaluated on three datasets from different domains and outperforms almost all previous work.  Strengths:  The paper is overall very well-written. The explanation of system is clear, and the analysis is thorough.  The system itself is a natural extension of various ideas. The most similar work include tree-based generation with parent feeding (Dong and Lapata, 2016) and various RNN-based semantic parsing with copy mechanism (Jia and Liang, 2016; Ling et al., 2016). [The guidance of parsing based on grammar is also explored in Chen Liang et al., 2016 ([Link] where a code-assist system is used to ensure that the code is valid.] Nevertheless, the model is this paper stands out as it is able to generate much longer and more complex programs than most previous work mentioned.   Weaknesses:  The evaluation is done on code accuracy (exact match) and BLEU score. These metrics (especially BLEU) might not be the best metrics for evaluating the correctness of programs. For instance, the first example in Table 5 shows that while the first two lines in boxes A and B are different, they have the same semantics. Another example is that variable names can be different. Evaluation based on what the code does (e.g., using test cases or static code analysis) would be more convincing.  Another point about evaluation: other systems (e.g., NMT baseline) may generate code with syntactic error. Would it be possible to include the result on the highest-scoring well-formed code (e.g., using beam search) that these baseline systems generate? This would give a fairer comparison since these system can choose to prune malformed code.  General Discussion:  * Lines 120-121: some approaches that use domain-specific languages were also guided by a grammar. One example is Berant and Liang, 2014, which uses a pretty limited grammar for logical forms (Table 1). In addition to comparing to that line of work, emphasizing that the grammar in this paper is much larger than most previous work would make this work stronger.  * Lines 389-397: For the parent feeding mechanism, is the child index being used? In other words, is p_t different when generating a first child versus a second child? In Seq2Tree (Dong and Lapata, 2016) the two non-terminals would have different hidden states.  * Line 373: Are the possible tokens embedded? Is it assumed that the set of possible tokens is known beforehand?  * The examples in the appendix are nice.  ---  I have read the author response.
[A]: en


[Q]: This paper creates a physics simulator using theano, and uses it to learn a neural network policy by back propagating gradients through the simulation. The approach is novel, and is motivated by being able to learn policies for robotics.  My two key reservations with the paper are as follows: 1. The method is motivated by learning policies for robotics. However, the proposed method is *only* useful for robotics if the learned policy can transfer the real world. Transferring policies from simulation to real-world is an open research problem, and is particularly challenging with less realistic simulators. 2. They key novelty/benefit of this approach over other model-based approaches is that the simulator is differentiable. However, the only empirical comparison in the paper is to a model-free approach (CMA-ES). To appropriately demonstrate the approach, it should be compared to other model-based approaches, which do not require analytic derivatives of the model.  For the reader to fully understand the pros and cons of the approach, the paper should also include quantitative comparison between the speed of the proposed simulator, and that of standard simulation platforms.  Because the idea is interesting and novel, I think it lies above the acceptance threshold. However, it would be significantly improved with the aforementioned comparisons.  Lastly, the writing of the paper could be improved, as it is rather informal and/or imprecise in a number of places. Here are some examples: -- “we model the use of a neural network as a general controller for a robot” - can be more concisely phrased as something like “we model the robot controller using a neural network” or “the robot controller is modeled using a neural network" -- “In previous research, finding a gradient…” - This is a run-on sentence. -- “We basically jam this entire equation into” - This sentence is informal and imprecise. -- “deep learning neural network” - the word “learning” should be omitted -- “one big RNN, where we unfold over time” - should be “…RNN, which we unfold over time” or “…RNN, unfolded over time”  The writing would also be improved by making it more concise and fitting the paper into 8 pages.
[A]:
en