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] 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


[Q]: Summary: The paper proposes a large-scale dataset for reading comprehension, with the final goal of releasing 1 million questions and answers. The authors have currently released 100,000 queries and their answers. The dataset differs from existing reading comprehension datasets mainly w.r.t queries being sampled from user queries rather than being generated by crowd-workers and answers being generated by crowd-workers rather than being spans of text from the provided passage. The paper presents some analysis of the dataset such as distribution of answer types. The paper also presents the results of some generative and some cloze-style models on the MS MARCO dataset.  Strengths:  1. The paper provides useful insights about the limitations of the existing reading comprehension datasets – questions asked by crowd-workers have different distribution compared to that of questions asked by actual users of intelligent agents, answers being restricted to span from the reading text rather than requiring reasoning across multiple pieces of text/passages.  2. MS MARCO dataset has novel useful characteristics compared to existing reading comprehension datasets – questions are sampled from user queries, answers are generated by humans.  3. The experimental evaluation of the existing baseline models on the MS MARCO dataset is satisfactory.  Weaknesses/Suggestions:  1. The paper does not report human performance on the dataset. Human performance should be reported to estimate the difficulty of the dataset. The degree of inter-human agreement will also reflect how well the metric (being used to compute inter-human agreement and accuracies of the baseline models) can deal with variance in the sentence structure with similar semantics.  2. I would like to see the comparison between the answer type distribution in the MS MARCO dataset and that in existing reading comprehension datasets such as SQuAD. This would ground the claim made in the paper the distributions of questions asked by crowd-workers is different from that of user queries.  3. The paper uses automatic metrics such as ROUGE, BLEU for evaluating natural language answers. However, it is known that such metrics poorly correlate with human judgement for tasks such as image caption evaluation (Chen et al., Microsoft COCO Captions: Data Collection and Evaluation Server, CoRR abs/1504.00325 (2015)). So, I wonder how authors justify using such metrics for evaluating open-ended natural language answers.  4. The paper mentions that a classifier was used to filter answer seeking queries from all Bing queries. It would be good to mention the accuracy of this classifier. This will provide insights into what percentage of the MS MARCO questions are answer seeking queries. Similarly, what is the accuracy of the information retrieval based system used to retrieve passages for filtered queries?  5. Please include the description of the best passage baseline in the paper.    6. Fix opening quotes, i.e. ” -> “ (for instance, on page 5, ”what” -> “what”).  Review Summary: The paper is well motivated, the use of user queries and human generated answers makes the dataset different from existing datasets. However, I would like to see the human performance on the dataset and quantitative comparison between the distribution of questions obtained from user queries and that of crowd-sourced questions. I would also like the authors to comment on the use of automatic metrics (such as ROUGE, BLEU) in the light of the fact that such metrics do not correlate well with human judgements for tasks such as image caption evaluation.
[A]: en


[Q]: - Overview:  The paper proposes a new model for training sense embeddings grounded in a lexical-semantic resource (in this case WordNet). There is no direct evaluation that the learned sense vectors are meaningful; instead, the sense vectors are combined back into word embeddings, which are evaluated in a downstream task: PP attachment prediction.  - Strengths:  PP attachment results seem solid.  - Weaknesses:  Whether the sense embeddings are meaningful remains uninvestigated.   The probabilistic model has some details that are hard to understand. Are the \lambda_w_i hyperparameters or trained? Where does “rank” come from, is this taken from the sense ranks in WordNet?  Related work: the idea of expressing embeddings of words as a convex combination of sense embeddings has been proposed a number of times previously. For instance, Johansson and Nieto Piña “Embedding a semantic network in a word space” (NAACL, 2015) decomposed word embeddings into ontology-grounded sense embeddings based on this idea. Also in unsupervised sense vector training this idea has been used, for instance by Arora et al “Linear Algebraic Structure of Word Senses, with Applications to Polysemy”.  Minor comments:  no need to define types and tokens, this is standard terminology  why is the first \lamba_w_i in equation 4 needed if the probability is unnormalized?  - General Discussion:
[A]:
en