Task: Language Identification
Input (Spanish, English): *** Paper Summary ***  This paper simplify matching network by considering only a single prototype per class which is obtained as the average of the embedding of the training class samples. Empirical comparisons with matching networks are reported.  *** Review ***  The paper reads well and clearly motivate the work. This work of learning metric learning propose to simplify an earlier work (matching network) which is a great objective. However, I am not sure it achieve better results than matching networks. The space of learning embeddings to optimize nearest neighbor classification has been explored before, but the idea of averaging the propotypes is interesting (as a non-linear extension of Mensink et al 2013). I would suggest to improve the discussion of related work and to consolidate the results section to help distinguish between the methods you outperform and the one you do not.   The related work section can be extended to include work on learning distance metric to optimize a nearest neighbor classification, see Weinberger et al, 2005 and subsequent work. Extensions to perform the same task with neural networks can be found in Min et al, 09 that purse a goal very close to yours. Regarding approaches pursuing similar goals with a different learning objective, you cite siamese network with pairwise supervision. The learning to rank (for websearch) litterature with triplet supervision or global ranking losses is also highly relevant, ie. one example "the query" defines the class and the embedding space need to be such that positive/relevant document are closer to the query than the others. I would suggest to start with Chris Burges 2010 tutorial. One learning class   I am not sure the reported results correctly reflect the state of the art for all tasks. The results are positive on Omniglot but I feel that you should also report the better results of matching networks on miniImageNet with fine tuning and full contextual embeddings. It can be considered misleading not to report it. On Cub 200, I thought that the state-of-the-art was 50.1%, when using features from GoogLeNet (Akata et al 2015), could you comment on this?  Overall, paper could greatly be improved, both in the discussion of related work and with a less partial reporting of prior empirical results.  *** References ***  Large Margin Nearest Neighbors. Weinberger et al, 2005 From RankNet to LambdaRank to LambdaMART: An Overview, Chris J.C. Burges, June 23, 2010 A Deep Non-linear Feature Mapping for Large-Margin kNN Classification, Min et al, 09
Output (English): en