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267 | The experiments miss some of the more recent baseline in domain adaptation, such as Adversarial Discriminative Domain Adaptation (Tzeng, Eric, et al. 2017).[experiments-NEG], [SUB-NEG] | experiments | null | null | null | null | null | SUB | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
268 | It could be more meaningful to organize the pairs in table by target domain instead of source, for example, grouping 9->9, 8->9, 7->9 and 3->9 in the same block.[table-NEU], [PNF-NEG] | table | null | null | null | null | null | PNF | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
269 | DAuto does seem to offer more boost in domain pairs that are less similar.[null], [EMP-NEU]] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
271 | (1) The topic of this paper seems to have minimal connection with ICRL.[topic-NEG], [APR-NEG] | topic | null | null | null | null | null | APR | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
272 | It might be more appropriate for this paper to be reviewed at a control/optimization conference, so that all the technical analysis can be evaluated carefully.[paper-NEU], [APR-NEG] | paper | null | null | null | null | null | APR | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
273 | (2) I am not convinced if the main results are novel.[main results-NEG], [NOV-NEG] | main results | null | null | null | null | null | NOV | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
274 | The convergence of policy gradient does not rely on the convexity of the loss function, which is known in the community of control and dynamic programming.[null], [EMP-NEG] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
275 | The convergence of policy gradient is related to the convergence of actor-critic, which is essentially a form of policy iteration. [null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
276 | I am not sure if it is a good idea to examine the convergence purely from an optimization perspective.[idea-NEU], [EMP-NEG] | idea | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
277 | (3) The main results of this paper seem technical sound.[main results-POS], [EMP-POS] | main results | null | null | null | null | null | EMP | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
278 | However, the results seem a bit limited because it does not apply to neural-network function approximator. [results-NEG], [EMP-NEG] | results | null | null | null | null | null | EMP | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
279 | It does not apply to the more general control problem rather than quadratic cost function, which is quite restricted.[null], [EMP-NEG] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
281 | I strongly suggest that these results be submitted to a more suitable venue. [results-NEU], [APR-NEG] | results | null | null | null | null | null | APR | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
288 | The experimental results are very good and give strong support for the proposed normalization.[experimental results-POS], [EMP-POS] | experimental results | null | null | null | null | null | EMP | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
289 | While the main idea is not new to machine learning (or deep learning), to the best of my knowledge it has not been applied on GANs.[main idea-NEG], [NOV-NEG] | main idea | null | null | null | null | null | NOV | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
290 | The paper is overall well written (though check Comment 3 below), it covers the related work well and it includes an insightful discussion about the importance of high rank models.[paper-POS, related work-POS, discussion-POS, models-POS], [CLA-POS, SUB-POS, CMP-POS] | paper | related work | discussion | models | null | null | CLA | SUB | CMP | null | null | POS | POS | POS | POS | null | null | POS | POS | POS | null | null |
291 | I am recommending acceptance,[null], [REC-POS] | null | null | null | null | null | null | REC | null | null | null | null | null | null | null | null | null | null | POS | null | null | null | null |
292 | though I anticipate to see a more rounded evaluation of the exact mechanism under which SN improves over the state of the art.[evaluation-NEU], [SUB-NEU] | evaluation | null | null | null | null | null | SUB | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
294 | Comments: 1. One concern about this paper is that it doesn't fully answer the reasons why this normalization works better.[paper-NEG], [SUB-NEG] | paper | null | null | null | null | null | SUB | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
295 | I found the discussion about rank to be very intuitive,[discussion-POS], [EMP-POS] | discussion | null | null | null | null | null | EMP | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
296 | however this intuition is not fully tested.[null], [SUB-NEG] | null | null | null | null | null | null | SUB | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
298 | The authors claim that other methods, like (Arjovsky et al. 2017) also suffer from the same rank deficiency.[methods-NEU], [EMP-NEU] | methods | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
301 | One way to test the rank hypothesis and better explain this method is to run a couple of truncated-SN experiments.[method-NEU, experiments-NEU], [EMP-NEU] | method | experiments | null | null | null | null | EMP | null | null | null | null | NEU | NEU | null | null | null | null | NEU | null | null | null | null |
302 | What happens if you run your SN but truncate its spectrum after every iteration in order to make it comparable to the rank of WN? Do you get comparable inception scores? Or does SN still win?[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
303 | 3. Section 4 needs some careful editing for language and grammar. [Section-NEU, grammar-NEG], [CLA-NEG] | Section | grammar | null | null | null | null | CLA | null | null | null | null | NEU | NEG | null | null | null | null | NEG | null | null | null | null |
310 | Some suggestions / criticisms are given below. 1) The findings seem conceptually similar to the older sparse coding ideas from the visual cortex.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
311 | That connection might be worth discussing because removing the regularizing (i.e., metabolic cost) constraint from your RNNS makes them learn representations that differ from the ones seen in EC.[null], [EMP-POS] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | POS | null | null | null | null |
312 | The sparse coding models see something similar: without sparsity constraints, the image representations do not resemble those seen in V1, but with sparsity, the learned representations match V1 quite well.[null], [EMP-POS] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | POS | null | null | null | null |
313 | That the same observation is made in such disparate brain areas (V1, EC) suggests that sparsity / efficiency might be quite universal constraints on the neural code.[null], [EMP-POS] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | POS | null | null | null | null |
314 | 2) The finding that regularizing the RNN makes it more closely match the neural code is also foreshadowed somewhat by the 2015 Nature Neuro paper by Susillo et al.[finding-NEU], [CMP-NEU] | finding | null | null | null | null | null | CMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
319 | 3) Why the different initializations for the recurrent weights for the hexagonal vs other environments?[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
320 | I'm guessing it's because the RNNs don't work in all environments with the same initialization (i.e., they either don't look like EC, or they don't obtain small errors in the navigation task).[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
321 | That seems important to explain more thoroughly than is done in the current text.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
322 | 4) What happens with ongoing training?[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
324 | With on-going (continous) training, do the RNN neurons' spatial tuning remain stable, or do they continue to drift (so that border cells turn into grid cells turn into irregular cells, or some such)? [null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
325 | That result could make some predictions for experiment, that would be testable with chronic methods (like Ca2+ imaging) that can record from the same neurons over multiple experimental sessions.[result-NEU, experiment-NEU], [EMP-NEU] | result | experiment | null | null | null | null | EMP | null | null | null | null | NEU | NEU | null | null | null | null | NEU | null | null | null | null |
326 | 5) It would be nice to more quantitatively map out the relation between speed tuning, direction tuning, and spatial tuning (illustrated in Fig. 3).[Fig-NEU], [SUB-NEU] | Fig | null | null | null | null | null | SUB | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
327 | Specifically, I would quantify the cells' direction tuning using the circular variance methods that people use for studying retinal direction selective neurons.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
328 | And I would quantify speed tuning via something like the slope of the firing rate vs speed curves.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
329 | And quantify spatial tuning somehow (a natural method would be to use the sparsity measures sometimes applied to neural data to quantify how selective the spatial profile is to one or a few specific locations).[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
330 | Then make scatter plots of these quantities against each other.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
331 | Basically, I'd love to see the trends for how these types of tuning relate to each other over the whole populations: those trends could then be tested against experimental data (possibly in a future study).[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
338 | The reasoning here is that the image feature space may not be semantically organized so that we are not guaranteed that a small perturbation of an image vector will yield image vectors that correspond to semantically similar images (belonging to the same class).[reasoning-NEU], [EMP-NEU] | reasoning | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
346 | They claim that these augmentation types provide orthogonal benefits and can be combined to yield superior results.[results-NEU], [EMP-NEU] | results | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
347 | Overall I think this paper addresses an important problem in an interesting way,[paper-POS, problem-NEU], [EMP-POS] | paper | problem | null | null | null | null | EMP | null | null | null | null | POS | NEU | null | null | null | null | POS | null | null | null | null |
348 | but there is a number of ways in which it can be improved, detailed in the comments below.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
349 | Comments: -- Since the authors are using a pre-trained VGG for to embed each image, I'm wondering to what extent they are actually doing one-shot learning here.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
350 | In other words, the test set of a dataset that is used for evaluation might contain some classes that were also present in the training set that VGG was originally trained on.[dataset-NEU], [EMP-NEU] | dataset | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
352 | Can the VGG be instead trained from scratch in an end-to-end way in this model?[model-NEU], [EMP-NEU] | model | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
353 | -- A number of things were unclear to me with respect to the details of the training process: the feature extractor (VGG) is pre-trained.[training process-NEU], [CLA-NEG] | training process | null | null | null | null | null | CLA | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
354 | Is this finetuned during training?[training-NEU], [EMP-NEU] | training | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
355 | If so, is this done jointly with the training of the auto-encoder?[training-NEU], [EMP-NEU] | training | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
356 | Further, is the auto-encoder trained separately or jointly with the training of the one-shot learning classifier?[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
357 | -- While the authors have convinced me that data augmentation indeed significantly improves the performance in the domains considered (based on the results in Table 1 and Figure 5a),[performance-POS, results-POS, Table-NEU, Figure-NEU], [EMP-POS] | performance | results | Table | Figure | null | null | EMP | null | null | null | null | POS | POS | NEU | NEU | null | null | POS | null | null | null | null |
358 | I am not convinced that augmentation in the proposed manner leads to a greater improvement than just augmenting in the image feature domain.[improvement-NEU], [EMP-NEG] | improvement | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
359 | In particular, in Table 2, where the different types of augmentation are compared against each other, we observe similar results between augmenting only in the image feature space versus augmenting only in the semantic feature space (ie we observe that FeatG performs similarly as SemG and as SemN).[Table-NEU, results-NEG], [EMP-NEG] | Table | results | null | null | null | null | EMP | null | null | null | null | NEU | NEG | null | null | null | null | NEG | null | null | null | null |
360 | When combining multiple types of augmentation the results are better,[results-POS], [EMP-POS] | results | null | null | null | null | null | EMP | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
362 | Specifically, the authors say that for each image they produce 5 additional virtual data points, but when multiple methods are combined, does this mean 5 from each method? Or 5 overall? If it's the former, the increased performance may merely be attributed to using more data.[performance-NEU], [EMP-NEU] | performance | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
364 | -- Comparison with existing work: There has been a lot of work recently on one-shot and few-shot learning that would be interesting to compare against.[work-NEU], [CMP-NEU, SUB-NEU] | work | null | null | null | null | null | CMP | SUB | null | null | null | NEU | null | null | null | null | null | NEU | NEU | null | null | null |
365 | In particular, mini-ImageNet is a commonly-used benchmark for this task that this approach can be applied to for comparison with recent methods that do not use data augmentation.[benchmark-NEU, task-NEU, comparison-NEU], [CMP-NEU, SUB-NEU] | benchmark | task | comparison | null | null | null | CMP | SUB | null | null | null | NEU | NEU | NEU | null | null | null | NEU | NEU | null | null | null |
369 | -- A suggestion: As future work I would be very interested to see if this method can be incorporated into common few-shot learning models to on-the-fly generate additional training examples from the support set of each episode that these approaches use for training.[future work-NEU, method-NEU, approaches-NEU], [IMP-NEU] | future work | method | approaches | null | null | null | IMP | null | null | null | null | NEU | NEU | NEU | null | null | null | NEU | null | null | null | null |
373 | I like the presentation and writing of this paper.[presentation-POS, writing-POS], [CLA-POS, PNF-POS] | presentation | writing | null | null | null | null | CLA | PNF | null | null | null | POS | POS | null | null | null | null | POS | POS | null | null | null |
374 | However, I find it uneasy to fully evaluate the merit of this paper, mainly because the wide-layer assumption seems somewhat artificial and makes the corresponding results somewhat expected.[results-NEG], [EMP-NEG] | results | null | null | null | null | null | EMP | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
376 | This is not surprising.[null], [EMP-NEG] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
377 | It would be interesting to make the results more quantitive, e.g., to quantify the tradeoff between having local minimums and having nonzero training error. [results-NEU], [EMP-NEU] | results | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
380 | Overall, I feel that the paper is hard to understand and that it would benefit from more clarity, e.g., section 3.3 states that decoding from the softmax q-distribution is similar to the Bayes decision rule.[paper-NEG, section-NEU], [CLA-NEG, PNF-NEG] | paper | section | null | null | null | null | CLA | PNF | null | null | null | NEG | NEU | null | null | null | null | NEG | NEG | null | null | null |
381 | Please elaborate on this.[null], [SUB-NEU] | null | null | null | null | null | null | SUB | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
382 | Did you compare to minimum bayes risk decoding which chooses the output with the lowest expected risk amongst a set of candidates?[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
384 | However, the methods analyzed in this paper also require sampling (cf. Appendix D.2.4 where you mention a sample size of 10),[methods-NEU], [SUB-NEU, EMP-NEU] | methods | null | null | null | null | null | SUB | EMP | null | null | null | NEU | null | null | null | null | null | NEU | NEU | null | null | null |
385 | Please explain the difference.[difference-NEU], [SUB-NEU, EMP-NEU] | difference | null | null | null | null | null | SUB | EMP | null | null | null | NEU | null | null | null | null | null | NEU | NEU | null | null | null |
391 | An experimental comparison is needed.[experimental comparison-NEU], [CMP-NEU] | experimental comparison | null | null | null | null | null | CMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
392 | Cotterell et al., EACL 2017 Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis: This paper also derives a tensor factorization based approach for learning word embeddings for different covariates.[paper-NEU], [EMP-NEU] | paper | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
394 | Due to these two citations, the novelty of both the problem set-up of learning different embeddings for each covariate and the novelty of the tensor factorization based model are limited.[citations-NEG, novelty-NEG], [NOV-NEG] | citations | novelty | null | null | null | null | NOV | null | null | null | null | NEG | NEG | null | null | null | null | NEG | null | null | null | null |
395 | The writing is ok.[writing-NEU], [CLA-NEU] | writing | null | null | null | null | null | CLA | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
396 | I appreciated the set-up of the introduction with the two questions.[setup-POS, introduction-POS], [PNF-POS] | setup | introduction | null | null | null | null | PNF | null | null | null | null | POS | POS | null | null | null | null | POS | null | null | null | null |
397 | However, the questions themselves could have been formulated differently: Q1: the way Q1 is formulated makes it sound like the covariates could be both discrete and continuous while the method presented later in the paper is only for discrete covariates (i.e. group structure of the data).[questions-NEU], [EMP-NEU] | questions | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
398 | Q2: The authors mention topic alignment without specifying what the topics are aligned to.[null], [EMP-NEG] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
399 | It would be clearer if they stated explicitly that the alignment is between covariate-specific embeddings.[null], [CLA-NEU] | null | null | null | null | null | null | CLA | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
400 | It is also distracting that they call the embedding dimensions topics.[null], [EMP-NEG] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
402 | In the model section, the paragraphs otation and objective function and discussion are clear.[model section-POS], [CLA-POS] | model section | null | null | null | null | null | CLA | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
403 | I also liked the idea of having the section A geometric view of embeddings and tensor decomposition, but that section needs to be improved.[idea-POS, section-NEU], [EMP-POS] | idea | section | null | null | null | null | EMP | null | null | null | null | POS | NEU | null | null | null | null | POS | null | null | null | null |
404 | For example, the authors describe RandWalk (Arora et al. 2016) but how their work falls into that framework is unclear.[work-NEU], [CMP-NEG] | work | null | null | null | null | null | CMP | null | null | null | null | NEU | null | null | null | null | null | NEG | null | null | null | null |
405 | In the third paragraph, starting with Therefore we consider a natural extension of this model, ... it is unclear which model the authors are referring to. (RandWalk or their tensor factorization?).[model-NEG], [CMP-NEG, CLA-NEG] | model | null | null | null | null | null | CMP | CLA | null | null | null | NEG | null | null | null | null | null | NEG | NEG | null | null | null |
406 | What are the context vectors in Figure 1? [Figure-NEU], [EMP-NEU] | Figure | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
409 | In the last paragraph, beginning with Note that this is essentially saying..., I don't agree with the argument that the base embeddings decompose into independent topics.[paragraph-NEG], [EMP-NEG] | paragraph | null | null | null | null | null | EMP | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
410 | The dimensions of the base embeddings are some kind of latent attributes and each individual dimension could be used by the model to capture a variety of attributes.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
412 | Also, the qualitative results in Table 3 do not convince me that the embedding dimensions represent topics.[results-NEG], [EMP-NEG] | results | null | null | null | null | null | EMP | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
415 | Hence, the apparent semantic coherence in what the authors call topics.[null], [EMP-NEU] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEU | null | null | null | null |
416 | The authors present multiple qualitative and quantitative evaluations.[evaluations-POS], [SUB-POS] | evaluations | null | null | null | null | null | SUB | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
417 | The clustering by weight (4.1.) is nice and convincing that the model learns something useful.[model-POS], [EMP-POS] | model | null | null | null | null | null | EMP | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
418 | 4.2, the only quantitative analysis was missing some details.[quantitative analysis-NEG], [SUB-NEG] | quantitative analysis | null | null | null | null | null | SUB | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
419 | Please give references for the evaluation metrics used, for proper credit and so people can look up these tasks.[references-NEG, tasks-NEU], [SUB-NEG] | references | tasks | null | null | null | null | SUB | null | null | null | null | NEG | NEU | null | null | null | null | NEG | null | null | null | null |
420 | Also, comparison needed to fitting GloVe on the entire corpus (without covariates) and existing methods Rudolph et al. 2017 and Cotterell et al. 2017.[comparison-NEU], [CMP-NEU] | comparison | null | null | null | null | null | CMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
422 | However, for the covariate specific analogies (5.3.) the authors could also analyze word similarities without the analogy component and probably see similar qualitative results.[qualitative results-NEU], [EMP-NEU] | qualitative results | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
423 | Specifically, they could analyze for a set of query words, what the most similar words are in the embeddings obtained from different subsections of the data.[analyze-NEU], [EMP-NEU] | analyze | null | null | null | null | null | EMP | null | null | null | null | NEU | null | null | null | null | null | NEU | null | null | null | null |
425 | + the tensor factorization set-up ensures that the embedding dimensions are aligned + clustering by weights (4.1) is useful and seems coherent + covariate-specific analogies are a creative analysis[analysis-POS], [EMP-POS] | analysis | null | null | null | null | null | EMP | null | null | null | null | POS | null | null | null | null | null | POS | null | null | null | null |
426 | CONS: - problem set-up not novel and existing approach not cited (experimental comparison needed)[problem setup-NEG], [NOV-NEG] | problem setup | null | null | null | null | null | NOV | null | null | null | null | NEG | null | null | null | null | null | NEG | null | null | null | null |
427 | - interpretation of embedding dimensions as topics not convincing[null], [EMP-NEG] | null | null | null | null | null | null | EMP | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
428 | - connection to Rand-Walk (Aurora 2016) not stated precisely enough[null], [CLA-NEG] | null | null | null | null | null | null | CLA | null | null | null | null | null | null | null | null | null | null | NEG | null | null | null | null |
429 | - quantitative results (Table 1) too little detail: * why is this metric appropriate[quantitative results-NEG], [EMP-NEU] | quantitative results | null | null | null | null | null | EMP | null | null | null | null | NEG | null | null | null | null | null | NEU | null | null | null | null |