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: In this paper, the authors propose a new method to learn hierarchical representations of sentences, based on reinforcement learning. They propose to learn a neural shift-reduce parser, such that the induced tree structures lead to good performance on a downstream task. They use reinforcement learning (more specifically, the policy gradient method REINFORCE) to learn their model. The reward of the algorithm is the evaluation metric of the downstream task. The authors compare two settings, (1) no structure information is given (hence, the only supervision comes from the downstream task) and (2) actions from an external parser is used as supervision to train the policy network, in addition to the supervision from the downstream task. The proposed approach is evaluated on four tasks: sentiment analysis, semantic relatedness, textual entailment and sentence generation.  I like the idea of learning tree representations of text which are useful for a downstream task. The paper is clear and well written. However, I am not convinced by the experimental results presented in the paper. Indeed, on most tasks, the proposed model is far from state-of-the-art models:  - sentiment analysis, 86.5 v.s. 89.7 (accuracy);  - semantic relatedness, 0.32 v.s. 0.25 (MSE);  - textual entailment, 80.5 v.s. 84.6 (accuracy). From the results presented in the paper, it is hard to know if these results are due to the model, or because of the reinforcement learning algorithm.  PROS:  - interesting idea: learning structures of sentences adapted for a downstream task.  - well written paper. CONS:  - weak experimental results (do not really support the claim of the authors).  Minor comments: In the second paragraph of the introduction, one might argue that bag-of-words is also a predominant approach to represent sentences. Paragraph titles (e.g. in section 3.2) should have a period at the end.  ---------------------------------------------------------------------------------------------------------------------- UPDATE  I am still not convinced by the results presented in the paper, and in particular by the fact that one must combine the words in a different way than left-to-right to obtain state of the art results. However, I do agree that this is an interesting research direction, and that the results presented in the paper are promising. I am thus updating my score from 5 to 6.

A: en
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Q: This work reframes paragraph vectors from a generative point of view and in so doing, motivates the existing method of inferring paragraph vectors as well as applying a L2 regularizer on the paragraph embeddings. The work also motivates joint learning of a classifier on the paragraph vectors to perform text classification.  The paper has numerous citation issues both in formatting within the text and the formatting of the bibliography, e.g. on some occasions including first names, on others not. I suggest the authors use a software package like BibTex to have a more consistent bibliography. There seems to be little novelty in this work.   The authors claim that there is no proposed method for inferring unseen documents for paragraph vectors. This is untrue. In the original paragraph vector paper, the authors show that to get a new vector, the rest of the model parameters are held fixed and gradient descent is performed on the new paragraph vector. This means the original dataset is not needed when inferring a paragraph vector for new text. This work seems to be essentially doing the same thing when finding the MAP estimate for a new vector. Thus the only contribution from the generative paragraph vector framing is the regularization on the embedding matrix.  The supervised generative paragraph vector amounts to jointly training a linear classifier on the paragraph vectors, while inference for the paragraph vector is unchanged. For the n-gram based approach, the authors should cite Li et al., 2015.  In the experiments, table 1 and 2 are badly formatted with .0 being truncated. The authors also do not state the size of the paragraph vector. Finally the SGPV results are actually worse than that reported in the original paragraph vector paper where SST-1 got 48.7 and SST-2 got 86.3.  Bofang Li, Tao Liu, Xiaoyong Du, Deyuan Zhang, Zhe Zhao, Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews, 2015.

A: en
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Q: [Summary] This paper proposes a new way for knowledge base completion which highlights: 1) adopting an implicit shared memory, which makes no assumption about its structure and is completely learned during training; 2) modeling a multi-step search process that can decide when to terminate.  The experimental results on WN18 and FB15k seem pretty good. The authors also perform an analysis on a shortest path synthetic task, and demonstrate that this model is better than standard seq2seq.  The paper is well-written and it is easy to follow.  [Major comments] I actually do like the idea and am also impressed that this model can work well. The main concern is that this paper presents too little analysis about how it works and whether it is sensitive to the hyper-parameters, besides that only reporting a final model on WN18 and FB15k.  One key hyper-parameter I believe is the size of shared memory (using 64 for the experiments). I don’t think that this number should be fixed for all tasks, at least it should depend on the KB scale. Could you verify this in your experiments? Would it be even possible to make a memory structure with dynamic size?  The RL setting (stochastic search process) is also one highlight of the paper, but could you demonstrate that how much it does really help? I think it is necessary to compare to the following: remove the termination gate and fix the number of inference steps and see how well the model does? Also show how the performance varies on # of steps?  I appreciate your attempts on the shortest path synthetic task. However, I think it would be much better if you can demonstrate that under a real KB setting. You can still perform the shortest path analysis, but using KB  (e.g., Freebase) entities and relations.  [Minor comments] I am afraid that the output gate illustrated in Figure 1 is a bit confusing. There should be only one output, depending on when the search process is terminated.

A:
en
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