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]: 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]: - Strengths: The authors present a novel adaptation of encoder-decoder neural MT using an approach that starts and ends with characters, but in between works with representations of morphemes and characters. The authors release both their code as well as their final learned models for fr-en, cs-en, and en-cs. This is helpful in validating their work, as well as for others looking to replicate and extends this work. The system reported appears to produce translation results of reasonable quality even after the first training epoch, with continued progress in future epochs. The system appears to learn reasonable morphological tokenizations, and appears able to handle previously unseen words (even nonce words) by implicitly backing off to morphemes. - Weaknesses: In the paper, the authors do not explicitly state which WMT test and dev sets their results are reported on. This is problematic for readers wishing to compare the reported results to existing work (for example, the results at matrix.statmt.org). The only way this reviewer found to get this information was to look in the README of the code supplement, which indicates that the test set was newstest2015 and the dev test was newstest2013. This should have been explicitly described in the paper. The instructions given in the software README are OK, but not great. The training and testing sections each could be enhanced with explicit examples of how to run the respective commands. The software itself should respond to a --help flag, which it currently does not. The paper describes a 6-level architecture, but the diagram in Figure 2 appears to show fewer than 6 layers. What's going on? The caption should be more explicit, and if this figure is not showing all of the layers, then there should be a figure somewhere (even if it's in an appendix) showing all of the layers. The results show comparison to other character-based neural systems, but do not show state-of-the-art results for other types of MT system. WMT (and matrix.statmt.org) has reported results for other systems on these datasets, and it appears that the state-of-the-art is much higher than any of the results reported in this paper. That should be acknowledged, and ideally should be discussed. There are a handful of minor English disfluencies, misspellings, and minor LaTeX issues, such as reverse quotation marks. These should be corrected. - General Discussion: Paper is a nice contribution to the existing literature on character-based neural MT.
[A]: en

[Q]: This paper explores the topic of intrinsic motivation in the context of deep RL. It proposes a couple of variants derived from an auxiliary model-learning process (prediction error, surprise and learning progress), and shows that those can help exploration on a number of continuous control tasks (and the Atari game "venture", maybe). Novelty: none of the proposed types of intrinsic motivation are novel, and it's arguable whether the application to deep RL is novel (see e.g. Kompella et al 2012). Potential: the idea of seeking out states where a transition model is uncertain is sensible, but also limited -- I would encourage the authors to also discuss the limitations. For example in a game like Go the transition model is trivially learned, so this approach would revert to random exploration. So other forms of learning progress or surprise derived from the agent's competence instead might be more promising in the long run? See also Srivastava et al 2012 for further thoughts. Computation time: I find the paper's claimed superiority over VIME to be overblown: the gain seems to stem almost exclusively from a faster initialization, but have very similar per-step cost? So given that VIME is also performing very competitively, what arguments can you advance for your own method(s)?
[A]:
en