instruction:
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].
question:
- El articulo describe varios enfoques para elaborar requisitos, y aplica uno de ellos (GAIA) a la construccion de sistemas multi-agentes (MAS). - La Introduccion es larguisima y cubre muchos temas; deberia ser dividida en varias secciones, y dejar en Introduccion solo el contexto general y descripcion del resto del articulo. - No hay un ejemplo para ilustrar la tecnica, que es descrita en 1,5 paginas, siendo lo esencial de la contribucion. - No hay validacion de tipo alguno.
answer:
es

question:
This work showed that word representation learning can benefit from sememes when used in an appropriate attention scheme. Authors hypothesized that sememes can act as an essential regularizer for WRL and WSI tasks and proposed SE-WL model which detects word senses and learn representations simultaneously. Though experimental results indicate that WRL benefits, exact gains for WSI are unclear since a qualitative case study of a couple of examples has only been done. Overall, paper is well-written and well-structured. In the last paragraph of introduction section, authors tried to tell three contributions of this work. (1) and (2) are more of novelties of the work rather than contributions. I see the main contribution of the work to be the results which show that we can learn better word representations (unsure about WSI) by modeling sememe information than other competitive baselines. (3) is neither a contribution nor a novelty. The three strategies tried for SE-WRL modeling makes sense and can be intuitively ranked in terms of how well they will work. Authors did a good job explaining that and experimental results supported the intuition but the reviewer also sees MST as a fourth strategy rather than a baseline inspired by Chen et al. 2014 (many WSI systems assume one sense per word given a context). MST many times performed better than SSA and SAC. Unless authors missed to clarify otherwise, MST seems to be exactly like SAT with a difference that target word is represented by the most probable sense rather than taking an attention weighted average over all its senses. MST is still an attention based scheme where sense with maximum attention weight is chosen though it has not been clearly mentioned if target word is represented by chosen sense embedding or some function of it. Authors did not explain the selection of datasets for training and evaluation tasks. Reference page to Sogou-T text corpus did not help as reviewer does not know Chinese language. It was unclear which exact dataset was used as there are several datasets mentioned on that page. Why two word similarity datasets were used and how they are different (like does one has more rare words than another) since different models performed differently on these datasets. The choice of these datasets did not allow evaluating against results of other works which makes the reviewer wonder about next question. Are proposed SAT model results state of the art for Chinese word similarity? E.g. Schnabel et al. (2015) report a score of 0.640 on WordSim-353 data by using CBOW word embeddings. Reviewer needs clarification on some model parameters like vocabulary sizes for words (Does Sogou-T contains 2.7 billion unique words) and word senses (how many word types from HowNet). Because of the notation used it is not clear if embeddings for senses and sememes for different words were shared. Reviewer hopes that is the case but then why 200 dimensional embeddings were used for only 1889 sememes. It would be better if complexity of model parameters can also be discussed. May be due to lack of space but experiment results discussion lack insight into observations other than SAT performing the best. Also, authors claimed that words with lower frequency were learned better with sememes without evaluating on a rare words dataset. I have read author's response.
answer:
en

question:
This paper describes how to approximate the FastText approach such that its memory footprint is reduced by several orders of magnitude, while preserving its classification accuracy. The original FastText approach was based on a linear classifier on top of bag-of-words embeddings. This type of method is extremely fast to train and test, but the model size can be quite large. This paper focuses on approximating the original approach with lossy compression techniques. Namely, the embeddings and classifier matrices A and B are compressed with Product Quantization, and an aggressive dictionary pruning is carried out. Experiments on various datasets (either with small or large number of classes) are conducted to tune the parameters and demonstrate the effectiveness of the approach. With a negligible loss in classification accuracy, an important reduction in term of model size (memory footprint) can be achieved, in the order of 100~1000 folds compared to the original size. The paper is well written overall. The goal is clearly defined and well carried out, as well as the experiments. Different options for compressing the model data are evaluated and compared (e.g. PQ vs LSH), which is also interesting. Nevertheless the paper does not propose by itself any novel idea for text classification. It just focuses on adapting existing lossy compression techniques, which is not necessarily a problem. Specifically, it introduces: - a straightforward variant of PQ for unnormalized vectors, - dictionary pruning is cast as a set covering problem (which is NP-hard), but a greedy approach is shown to yield excellent results nonetheless, - hashing tricks and bloom filter are simply borrowed from previous papers. These techniques are quite generic and could as well be used in other works. Here are some minor problems with the paper: - it is not made clear how the full model size is computed. What is exactly in the model? Which proportion of the full size do the A and B matrices, the dictionary, and the rest, account for? It is hard to follow where is the size bottleneck, which also seems to depend on the target application (i.e. small or large number of test classes). It would have been nice to provide a formula to calculate the total model size as a function of all parameters (k,b for PQ and K for dictionary, number of classes). - some parts lack clarity. For instance, the greedy approach to prune the dictionary is exposed in less than 4 lines (top of page 5), though it is far from being straightforward. Likewise, it is not clear why the binary search used for the hashing trick would introduce an overhead of a few hundreds of KB. Overall this looks like a solid work, but with potentially limited impact research-wise.
answer:
en