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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].
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This paper proposes a design principle for computation blocks in convolutional networks based on repeated application of expand and join operations resulting in a fractal-like structure. This paper is primarily about experimental evaluation, since the objective is to show that a residual formulation is not necessary to obtain good performance, at least on some tasks. However, in my opinion the evaluations in the paper are not convincing. The primary issue is lack of a proper baseline, against which the improvements can be clearly demonstrated by making isolated changes. I understand that for this paper such a baseline is hard to construct, since it is about a novel architecture principle. This is why more effort should be put into this, so that core insights from this paper can be useful even after better performing architectures are discovered. The number of parameters and amount of computation should be used to indicate how fair the comparisons are between architectures. Some detailed comments: - In Table 1 comparisons to Resnets, the resnets from He et al. 2016b and Wide Resnets should be compared to FractalNet (in lieu of a proper baseline). The first outperforms FractalNet on CIFAR-100 while the second outperforms it on both. The authors compare to other results without augmentation, but did not perform additional experiments without augmentation for these architectures. - The 40 layer Fractal Net should not be compared to other models unless the parameter reduction tricks are utilized for the other models as well. - A proper comparison to Inception networks should also be performed for these networks. My guess is that the reason behind a seemingly 'ad-hoc' design of Inception modules is to reduce the computational footprint of the model (which is not a central motivation of fractal nets). Since this model is directly related to the Inception module due to use of shorter and longer paths without shortcuts, one can easily simplify the Inception design to build a strong baseline e.g. by converting the concatenation operation to a mean operation among equally sized convolution outputs. As an aside, note that Inception networks have already shown that residual networks are not necessary to obtain the best performance [1]. - It should be noted that Residual/Highway architectures do have a type of anytime property, as shown by lesioning experiments in Srivastava et al and Viet et al. - The architecture specific drop-path regularization is interesting, but is used along with other regularizers such as dropout, batch norm and weight decay and its benefit on its own is not clear. Overall, it's not clear to me that the experiments clearly demonstrate the utility of the proposed architecture. [1] Szegedy, Christian, Sergey Ioffe, and Vincent Vanhoucke. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016).
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en

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This paper introduces pointer-network neural networks, which are applied to referring expressions in three small-scale language modeling tasks: dialogue modeling, recipe modeling and news article modeling. When conditioned on the co-reference chain, the proposed models outperform standard sequence-to-sequence models with attention. The proposed models are essentially variants of pointer networks with copy mechanisms (Gulcehre et al., 2016; Gu et al., 2016; Ling et al., 2016), which have been modified to take into account reference chains. As such, the main architectural novelty lies in 1) restricting the pointer mechanism to focus on co-referenced entities, 2) applying pointer mechanism to 2D arrays (tables), and 3) training with supervised alignments. Although useful in practice, these are minor contributions from an architectural perspective. The empirical contributions are centred around measuring perplexity on the three language modeling tasks. Measuring perplexity is typical for standard language modeling tasks, but is really an unreliable proxy for dialogue modeling and recipe generation performance. In addition to this, both the dialogue and recipe tasks are tiny compared to standard language modeling tasks. This makes it difficult to evaluate the impact of the dialogue and recipe modeling results. For example, if one was to bootstrap from a larger corpus, it seems likely that a standard sequence-to-sequence model with attention would yield performance comparable to the proposed models (with enough data, the attention mechanism could learn to align referring entities by itself). The language modeling task on news article (Gigaword) seems to yield the most conclusive results. However, the dataset for this task is non-standard and results are provided for only a single baseline. Overall, this limits the conclusions we can draw from the empirical experiments. Finally, the paper itself contains many errors, including mathematical errors, grammatical errors and typos: - Eq. (1) is missing a sum over $z_i$. - "into the a decoder LSTM" -> "into the decoder LSTM" - "denoted as his" -> "denoted as" - "Surprising," -> "Surprisingly," - "torkens" -> "tokens" - "if follows that the next token" -> "the next token" - In the "COREFERENCE BASED LANGUAGE MODEL" sub-section, what does $M$ denote? - In the sentence: "The attribute of each column is denoted as $s_c, where $c$ is the c-th attribute". For these definitions to be make sense, $s_c$ has to be a one-hot vector. If yes, please clarify this in the text. - "the weighted sum is performed" -> "the weighted sum is computed" - "a attribute" -> "an attribute" - In the paragraph on Pointer Switch, change $p(z_{i,v} |s_{i,v}) = 1$ -> $p(z_{i,v} |s_{i,v}) = 0$. - In the "Table Pointer" paragraph, I assume you mean outer-product instead of cross-product? Otherwise, I don't see how the equations add up. Other comments: - For the "Attention based decoder", is the attention computed using the word embeddings themselves or the hidden states of the sentence encoder? Also, it applied only to the previous turn of the dialogue or to the entire dialogue history? Please clarify this. - What's the advantage of using an "Entity state update" rule, compared to a pointer network or copy network, which you used in the dialogue and recipe tasks? Please elaborate on this. - In the Related Work section, the following sentence is not quite accurate: "For the task oriented dialogues, most of them embed the seq2seq model in traditional dialogue systems while our model queries the database directly.". There are task-oriented dialogue models which do query databases during natural language generation. See, for example, "A Network-based End-to-End Trainable Task-oriented Dialogue System" by Wen et al.
answer:
en

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I recommend to change the paper title in order to be according to paper' body Review the English grammar, some but not all of the errors founded in the review are: Abstract o what does it mean that for them?--> what does it mean that for them? o utilized to represent its appropriateness.--> utilized to represent their appropriateness. Introducction o has like first phase --> ? o auscultating --> is it a verb? Background o A systematic mapping study is a methodology that is used --> A systematic mapping study is a methodology used o of existing research on performance evaluation -->of existing research on the performance evaluation o Only Dieste and Juristo's work [10] deals --> Only the work of Dieste and Juristo [10] deals Methodology o Review the use of ly with the verb to be o Third question: What kind of studies does use with a higher frequency certain constructs for o appropriateness of techniques? --> ? review the types question structure Classification schema o In order to answer the research questions, the literature review focused --> literature or mapping? Analysis of the result o in Figure 1 en DCM format -->in Figure 1 en DCM format. o article --> paper Conclusions and future works o For this reason, it is necessary that more proposals are carried out to help --> For this reason, it is necessary to carry out more proposals to help Figures: be careful on the figure's title, the title must be following the figure Type of studies Paragraph after figure 5: Experimental (paragraph) o experiment (figure) Techniques considered: I recommend performing a mapping between domains and techniques and or with the constructors and show the a graphic of the results instead of the figure 8
answer:
en