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].

This paper presents an approach to tag word senses with temporal information (past, present, future or atemporal). They model the problem using a graph-based semi-supervised classification algorithm that allows to combine item specific information - such as the presence of some temporal indicators in the glosses - and the structure of Wordnet - that is semantic relations between synsets â, and to take into account unlabeled data. They perform a full annotation of Wordnet, based on a set of training data labeled in a previous work and using the rest of Wordnet as unlabeled data. Specifically, they take advantage of the structure of the label set by breaking the task into a binary formulation (temporal vs atemporal), then using the data labeled as temporal to perform a finer grained tagging (past, present or future). In order to intrinsically evaluate their approach, they annotate a subset of synsets in Wordnet using crowd-sourcing. They compare their system to the results obtained by a state-of-the-art time tagger (Stanford's SUTime) using an heuristic as a backup strategy, and to previous works. They obtain improvements around 11% in accuracy, and show that their approach allows performance higher than previous systems using only 400 labeled data. Finally, they perform an evaluation of their resource on an existing task (TempEval-3) and show improvements of about 10% in F1 on 4 labels.  This paper is well-constructed and generally clear, the approach seems sound and well justified. This work led to the development of a resource with fine grained temporal information at the word sense level that would be made available and could be used to improve various NLP tasks. I have a few remarks, especially concerning the settings of the experiments.  I think that more information should be given on the task performed in the extrinsic evaluation section. An example could be useful to understand what the system is trying to predict (the features describe âentity pairsâ but it has not been made clear before what are these pairs) and what are the features (especially, what are the entity attributes? What is the POS for a pair, is it one dimension or two? Are the lemmas obtained automatically?). The sentence describing the labels used is confusing, I'm not sure to understand what âevent to document creation timeâ and âevent to same sentence eventâ means, are they the kind of pairs considered? Are they relations (as they are described as relation at the beginning of p.8)? I find unclear the footnote about the 14 relations: why the other relations have to be ignored, what makes a mapping too âcomplexâ? Also, are the scores macro or micro averaged? Finally, the ablation study seems to indicate a possible redundancy between Lexica and Entity with quite close scores, any clue about this behavior?  I have also some questions about the use of the SVM.  For the extrinsic evaluation, the authors say that they optimized the parameters of the algorithm: what are these parameters?  And since a SVM is also used within the MinCut framework, is it optimized and how? Finally, if it's the LibSVM library that is used (Weka wrapper), I think a reference to LibSVM should be included.   Other remarks: - It would be interesting to have the number of examples per label in the gold data, the figures are given for coarse grained labels (127 temporal vs 271 atemporal), but not for the finer grained. - It would also be nice to have an idea of the number of words that are ambiguous at the temporal level, words like âpresentâ. - It is said in the caption of the table 3 that the results presented are âsignificantly betterâ but no significancy test is indicated, neither any p-value.  Minor remarks: - Related work: what kind of task was performed in (Filannino and Nenadic, 2014)? - Related work: ârequires a post-calibration procedureâ, needs a reference (and p.4 in 3.3 footnote it would be clearer to explain calibration) - Related work: âtheir model differ from oursâ, in what? - Table 3 is really too small: maybe, remove the parenthesis, put the â(p,r,f1)â in the caption and give only two scores, e.g. prec and f1. The caption should also be reduced. - Information in table 4 would be better represented using a graph. - Beginning of p.7: 1064 â 1264 - TempEval-3: reference ? - table 6: would be made clearer by ordering the scores for one column - p.5, paragraph 3: atemporal) â atemporal
en

Authors propose a competitive learning architecture that learn different RNN predictors independently, akin to a committee of experts which are chosen with a hard switch at run-time. This work is applied to the task of predictive different driving behaviors from human drivers, and combines behaviors at test time, often switching behaviors within seconds. Prediction loss is lower than the similar but non-competitive architecture used as a baseline. It is not very clear how to interpret the results, what is the real impact of the model. If behaviors switch very often, can this really be seen as choosing the best driving mode for a given situation? Maybe the motivation needs to be rephrased a little to be more convincing? The competitive approach presented is interesting but not really novel, thus the impact of this paper for a conference such as ICLR may be limited.
en

This paper proposes a novel strategy for zero-resource translation where (source, pivot) and (pivot, target) parallel corpora are available. A teacher model for p(target|pivot) is first trained on the (pivot, target) corpus, then a student model for p(target|source) is trained to minimize relative entropy with respect to the teacher on the (source, pivot) corpus. When using word-level relative entropy over samples from the teacher, this approach is shown to outperform previous variants on standard pivoting, as well as other zero-resource strategies.  This is a good contribution: a novel idea, clearly explained, and with convincing empirical support. Unlike some previous work, it makes fairly minimal assumptions about the nature of the NMT systems involved, and hence should be widely applicable.  I have only a few suggestions for further experiments. First, it would be interesting to see how robust this approach is to more dissimilar source and pivot languages, where intuitively the true p(target|source) and p(target|pivot) will be further apart. Second, given the success of introducing word-based diversity, it was surprising not to see a sentence n-best or sentence-sampling experiment. This would be more costly, but not much more so since you’re already doing beam search with the teacher. Finally, related to the previous, it might be interesting to explore transition from word-based diversity to sentence-based as the student converges and no longer needs the signal from low-probability words.  Some further comments:  line 241: Despite its simplicity -> Due to its simplicity  277: target sentence y -> target word y  442: I assume that K=1 and K=5 mean that you compare probabilities of the most probable and 5 most probable words in the current context. If so, how is the current context determined - greedily or with a beam?  Section 4.2. The comparison with an essentially uniform distribution doesn’t seem very informative here: it would be extremely surprising if p(y|z) were not significantly closer to p(y|x) than to uniform. It would be more interesting to know to what extent p(y|z) still provides a useful signal as p(y|x) gets better. This would be easy to measure by comparing p(y|z) to models for p(y|x) trained on different amounts of data or for different numbers of iterations. Another useful thing to explore in this section would be the effect of the mode approximation compared to n-best for sentence-level scores.  555: It’s odd that word beam does worse than word greedy, since word beam should be closer to word sampling. Do you have an explanation for this?  582: The claimed advantage of sent-beam here looks like it may just be noise, given the high variance of these curves.

en