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 proposes to use an encoder-decoder framework for keyphrase generation. Experimental results show that the proposed model outperforms other baselines if supervised data is available.  - Strengths: The paper is well-organized and easy to follow (the intuition of the proposed method is clear). It includes enough details to replicate experiments. Although the application of an encoder-decoder (+ copy mechanism) is straightforward, experimental results are reasonable and support the claim (generation of absent keyphrases) presented in this paper.  - Weaknesses: As said above, there is little surprise in the proposed approach. Also, as described in Section 5.3, the trained model does not transfer well to new domain (it goes below unsupervised models). One of the contribution of this paper is to maintain training corpora in good quantity and quality, but it is not (explicitly) stated.  - General Discussion: I like to read the paper and would be pleased to see it accepted. I would like to know how the training corpus (size and variation) affects the performance of the proposed method. Also, it would be beneficial to see the actual values of p_g and p_c (along with examples in Figure 1) in the CopyRNN model. From my experience in running the CopyNet, the copying mechanism sometimes works unexpectedly (not sure why this happens).
[EX A]: en

[EX Q]: This paper proposes a neural network architecture that represent structural linguistic knowledge in a memory network for sequence tagging tasks (in particular, slot-filling of the natural language understanding unit in conversation systems). Substructures (e.g. a node in the parse tree) is encoded as a vector (a memory slot) and a weighted sum of the substructure embeddings are fed in a RNN at each time step as additional context for labeling.  -----Strengths-----  I think the main contribution of this paper is a simple way to "flatten" structured information to an array of vectors (the memory), which is then connected to the tagger as additional knowledge. The idea is similar to structured / syntax-based attention (i.e. attention over nodes from treeLSTM); related work includes Zhao et al on textual entailment, Liu et al. on natural language inference, and Eriguchi et al. for machine translation. The proposed substructure encoder is similar to DCNN (Ma et al.): each node is embedded from a sequence of ancestor words. The architecture does not look entirely novel, but I kind of like the simple and practical approach compared to prior work.  -----Weaknesses-----  I'm not very convinced by the empirical results, mostly due to the lack of details of the baselines. Comments below are ranked by decreasing importance.  -  The proposed model has two main parts: sentence embedding and substructure embedding. In Table 1, the baseline models are TreeRNN and DCNN, they are originally used for sentence embedding but one can easily take the node/substructure embedding from them too. It's not clear how they are used to compute the two parts.  - The model uses two RNNs: a chain-based one and a knowledge guided one. The only difference in the knowledge-guided RNN is the addition of a "knowledge" vector from the memory in the RNN input (Eqn 5 and 8). It seems completely unnecessary to me to have separate weights for the two RNNs. The only advantage of using two is an increase of model capacity, i.e. more parameters. Furthermore, what are the hyper-parameters / size of the baseline neural networks? They should have comparable numbers of parameters.  - I also think it is reasonable to include a baseline that just input additional knowledge as features to the RNN, e.g. the head of each word, NER results etc.  - Any comments / results on the model's sensitivity to parser errors?  Comments on the model:  - After computing the substructure embeddings, it seems very natural to compute an attention over them at each word. Is there any reason to use a static attention for all words? I guess as it is, the "knowledge" is acting more like a filter to mark important words. Then it is reasonable to include the baseline suggest above, i.e. input additional features.  - Since the weight on a word is computed by inner product of the sentence embedding and the substructure embedding, and the two embeddings are computed by the same RNN/CNN, doesn't it means nodes / phrases similar to the whole sentence gets higher weights, i.e. all leaf nodes?  - The paper claims the model generalizes to different knowledge but I think the substructure has to be represented as a sequence of words, e.g. it doesn't seem straightforward for me to use constituent parse as knowledge here.  Finally, I'm hesitating to call it "knowledge". This is misleading as usually it is used to refer to world / external knowledge such as a knowledge base of entities, whereas here it is really just syntax, or arguably semantics if AMR parsing is used.  -----General Discussion-----  This paper proposes a practical model which seems working well on one dataset, but the main ideas are not very novel (see comments in Strengths). I think as an ACL paper there should be more takeaways. More importantly, the experiments are not convincing as it is presented now. Will need some clarification to better judge the results.  -----Post-rebuttal-----  The authors did not address my main concern, which is whether the baselines (e.g. TreeRNN) are used to compute substructure embeddings independent of the sentence embedding and the joint tagger. Another major concern is the use of two separate RNNs which gives the proposed model more parameters than the baselines. Therefore I'm not changing my scores.
[EX A]: en

[EX 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.
[EX A]:
en