paper_id stringlengths 18 19 | venue stringclasses 2
values | focused_review stringlengths 392 7.4k | point stringlengths 69 489 |
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ACL_2017_318_review | ACL_2017 | 1. Presentation and clarity: important details with respect to the proposed models are left out or poorly described (more details below). Otherwise, the paper generally reads fairly well; however, the manuscript would need to be improved if accepted.
2. The evaluation on the word analogy task seems a bit unfair given t... | 2. The evaluation on the word analogy task seems a bit unfair given that the semantic relations are explicitly encoded by the sememes, as the authors themselves point out (more details below). |
ARR_2022_236_review | ARR_2022 | - My main criticism is that the "mismatched" image caption dataset is artificial and may not capture the kind of misinformation that is posted on platforms like Twitter. For instance, someone posting a fake image of a lockdown at a particular place may not just be about a mismatch between the image and the caption, but... | - Also, since the dataset is artificially created, the dataset itself might have a lot of noise. For instance, the collected "pristine" set of tweets may not be pristine enough and might instead contain misinformation as well as out-of-context images. I would have liked to see more analysis around the quality of the co... |
ACL_2017_554_review | ACL_2017 | 1) The paper does not dig into the theory profs and show the convergence properties of the proposed algorithm.
2) The paper only shows the comparison between SG-MCMC vs RMSProp and did not conduct other comparison. It should explain more about the relation between pSGLD vs RMSProp other than just mentioning they are co... | 1) The paper does not dig into the theory profs and show the convergence properties of the proposed algorithm. |
ACL_2017_516_review | ACL_2017 | Missing related work on anchor words Evaluation on 20 Newsgroups is not ideal Theoretical contribution itself is small - General Discussion: The authors propose a new method of interactive user specification of topics called Tandem Anchors. The approach leverages the anchor words algorithm, a matrix-factorization appro... | - 261&272: any reason you did not consider the and operator or element-wise max? They seem to correspond to the ideas of union and intersection from the or operator and element-wise min, and it wasn’t clear to me why the ones you chose were better options. |
ACL_2017_588_review | ACL_2017 | and the evaluation leaves some questions unanswered. - Strengths: The proposed task requires encoding external knowledge, and the associated dataset may serve as a good benchmark for evaluating hybrid NLU systems.
- Weaknesses: 1) All the models evaluated, except the best performing model (HIERENC), do not have access ... | 3) The description of HIERENC is unclear. From what I understand, each input (h_i) to the temporal network is the average of the representations of all instantiations of context filled by every possible entity in the vocabulary. This does not seem to be a good idea since presumably only one of those instantiations is c... |
ARR_2022_23_review | ARR_2022 | The technical novelty is rather lacking. Although I believe this doesn't affect the contribution of this paper.
- You mention that you only select 10 answers from all correct answers, why do you do this? Does this affect the underestimation of the performances?
- Do you think generative PLMs that are pretrained on biom... | - You mention that you only select 10 answers from all correct answers, why do you do this? Does this affect the underestimation of the performances? |
ARR_2022_65_review | ARR_2022 | 1. The paper covers little qualitative aspects of the domains, so it is hard to understand how they differ in linguistic properties. For example, I think it is vague to say that the fantasy novel is more “canonical” (line 355). Text from a novel may be similar to that from news articles in that sentences tend to be com... | - Line 226-238 seem to suggest that the authors selected sentences from raw data of these sources, but line 242-244 say these already have syntactic information. If I understand correctly, the data selected is a subset of Li et al. (2019a)’s dataset. If this is the case, I think this description can be revised, e.g. me... |
ARR_2022_93_review | ARR_2022 | 1. From an experimental design perspective, the experimental design suggested by the authors has been used widely for open-domain dialogue systems with the caveat of it not being done in live interactive settings.
2. The authors have not referenced those works that use continuous scales in the evaluation and there is a... | 2. What is the purpose of the average duration reported in Table 1? There is no supporting explanation about it. Does it include time spent by the user waiting for the model to generate a response? |
ACL_2017_726_review | ACL_2017 | - Claims of being comparable to state of the art when the results on GeoQuery and ATIS do not support it. General Discussion: This is a sound work of research and could have future potential in the way semantic parsing for downstream applications is done. I was a little disappointed with the claims of “near-state-of-th... | - Table 4 needs a little more clarification, what splits are used for obtaining the ATIS numbers? I thank the authors for their response. |
ACL_2017_105_review | ACL_2017 | Maybe the model is just an ordinary BiRNN with alignments de-coupled.
Only evaluated on morphology, no other monotone Seq2Seq tasks.
- General Discussion: The authors propose a novel encoder-decoder neural network architecture with "hard monotonic attention". They evaluate it on three morphology datasets.
This paper is... | 4) You perform "on par or better" (l.791). There seems to be a general cognitive bias among NLP researchers to map instances where they perform worse to "on par" and all the rest to "better". I think this wording should be corrected, but otherwise I'm fine with the experimental results. |
ARR_2022_12_review | ARR_2022 | I feel the design of NVSB and some experimental results need more explanation (more information in the section below).
1. In Figure 1, given experimental dataset have paired amateur and professional recordings from the same singer, what are the main rationals for (a) Having a separate timbre encoder module (b) SADTW ta... | 2. For results shown in Table 3, how to interpret: (a) For Chinese MOS-Q, NVSB is comparable to GT Mel A. (b) For Chinese and English MOS-V, Baseline and NVSB have overlapping 95% CI. |
ARR_2022_311_review | ARR_2022 | __1. Lack of significance test:__ I'm glad to see the paper reports the standard deviation of accuracy among 15 runs. However, the standard deviation of the proposed method overlaps significantly with that of the best baseline, which raises my concern about whether the improvement is statistically significant. It would... | 1. Some items in Table 2 and Table 3 have Spaces between accuracy and standard deviation, and some items don't, which affects beauty. |
ACL_2017_818_review | ACL_2017 | 1) Many aspects of the approach need to be clarified (see detailed comments below). What worries me the most is that I did not understand how the approach makes knowledge about objects interact with knowledge about verbs such that it allows us to overcome reporting bias. The paper gets very quickly into highly technica... | 781 "both tasks": antecedent missing The references should be checked for format, e.g. Grice, Sorower et al for capitalization, the verbnet reference for bibliographic details. |
ARR_2022_317_review | ARR_2022 | - Lack of novelty: - Adversarial attacks by perturbing text has been done on many NLP models and image-text models. It is nicely summarized in related work of this paper. The only new effort is to take similar ideas and apply it on video-text models.
- Checklist (Ribeiro et. al., ACL 2020) had shown many ways to stress... | - Lack of novelty:- Adversarial attacks by perturbing text has been done on many NLP models and image-text models. It is nicely summarized in related work of this paper. The only new effort is to take similar ideas and apply it on video-text models. |
ACL_2017_31_review | ACL_2017 | ] See below for details of the following weaknesses: - Novelties of the paper are relatively unclear.
- No detailed error analysis is provided.
- A feature comparison with prior work is shallow, missing two relevant papers.
- The paper has several obscure descriptions, including typos.
[General Discussion:] The paper w... | - The explanations for features in Section 3.2 are somewhat intertwined and thus confusing. The section would be more coherently organized with more separate paragraphs dedicated to each of lexical features and sentence-level features, by: |
ACL_2017_779_review | ACL_2017 | However, there are many points that need to be address before this paper is ready for publication.
1) Crucial information is missing Can you flesh out more clearly how training and decoding happen in your training framework? I found out that the equations do not completely describe the approach. It might be useful to u... | 4) Not so useful information: While I appreciate the fleshing out of the assumptions, I find that dedicating a whole section of the paper plus experimental results is a lot of space. |
ARR_2022_82_review | ARR_2022 | - In the “Updating Facts” section, although the results seem to show that modifying the neurons using the word embeddings is effective, the paper lacks a discussion on this. It is not intuitive to me that there is a connection between a neuron at a middle layer and the word embeddings (which are used at the input layer... | - Using integrated gradients to measure the attribution has been studied in existing papers. The paper also proposes post-processing steps to filter out the “false-positive” neurons, however, the paper doesn’t show how important these post-processing steps are. I think an ablation study may be needed. |
ACL_2017_19_review | ACL_2017 | But I have a few questions regarding finding the antecedent of a zero pronoun: 1. How will an antecedent be identified, when the prediction is a pronoun? The authors proposed a method by matching the head of noun phrases. It’s not clear how to handle the situation when the head word is not a pronoun.
2. What if the pre... | 1. How will an antecedent be identified, when the prediction is a pronoun? The authors proposed a method by matching the head of noun phrases. It’s not clear how to handle the situation when the head word is not a pronoun. |
ACL_2017_318_review | ACL_2017 | 1. Presentation and clarity: important details with respect to the proposed models are left out or poorly described (more details below). Otherwise, the paper generally reads fairly well; however, the manuscript would need to be improved if accepted.
2. The evaluation on the word analogy task seems a bit unfair given t... | 3. It is unclear how the proposed models compare to models that only consider different senses but not sememes. Perhaps the MST baseline is an example of such a model? If so, this is not sufficiently described (emphasis is instead put on soft vs. hard word sense disambiguation). The paper would be stronger with the inc... |
ARR_2022_227_review | ARR_2022 | 1. The case made for adopting the proposed strategy for a new automated evaluation paradigm - auto-rewrite (where the questions that are not valid due to a coreference resolution failure in terms of the previous answer get their entity replaced to be made consistent with the gold conversational history) - seems weak. W... | - The abstract is written well and invokes intrigue early - could potentially be made even better if, for "evaluating with gold answers is inconsistent with human evaluation" - an example of the inconsistency, such as models get ranked differently is also given there. |
ACL_2017_818_review | ACL_2017 | - I would have liked to see more examples of objects pairs, action verbs, and predicted attribute relations. What are some interesting action verbs and corresponding attributes relations? The paper also lacks analysis/discussion on what kind of mistakes their model makes.
- The number of object pairs (3656) in the data... | - It's a bit unclear how the frame similarity factors and attributes similarity factors are selected. |
ACL_2017_699_review | ACL_2017 | 1. Some discussions are required on the convergence of the proposed joint learning process (for RNN and CopyRNN), so that readers can understand, how the stable points in probabilistic metric space are obtained? Otherwise, it may be tough to repeat the results.
2. The evaluation process shows that the current system (w... | 1. Some discussions are required on the convergence of the proposed joint learning process (for RNN and CopyRNN), so that readers can understand, how the stable points in probabilistic metric space are obtained? Otherwise, it may be tough to repeat the results. |
ACL_2017_818_review | ACL_2017 | 1) Many aspects of the approach need to be clarified (see detailed comments below). What worries me the most is that I did not understand how the approach makes knowledge about objects interact with knowledge about verbs such that it allows us to overcome reporting bias. The paper gets very quickly into highly technica... | 681 as mentioned above, you should discuss the results for the task of inferring knowledge on objects, and also include results for model (B) (incidentally, it would be better if you used the same terminology for the model in Tables 1 and 2) 778 "latent in verbs": why don't you mention objects here? |
ACL_2017_49_review | ACL_2017 | As always, more could be done in the experiments section to strengthen the case for chunk-based models. For example, Table 3 indicates good results for Model 2 and Model 3 compared to previous papers, but a careful reader will wonder whether these improvements come from switching from LSTMs to GRUs. In other words, it ... | - The sentence in line 212 ("We train a GRU that encodes a source sentence into a single vector") is not strictly correct. The correct way would be to say that you do a bidirectional encoder that encodes the source sentence into a set of vectors... at least, that's what I see in Figure 2. |
ACL_2017_494_review | ACL_2017 | - fairly straightforward extension of existing retrofitting work - would be nice to see some additional baselines (e.g. character embeddings) - General Discussion: The paper describes "morph-fitting", a type of retrofitting for vector spaces that focuses specifically on incorporating morphological constraints into the ... | - fairly straightforward extension of existing retrofitting work - would be nice to see some additional baselines (e.g. character embeddings) - |
ARR_2022_68_review | ARR_2022 | 1. Despite the well-motivated problem formulation, the simulation is not realistic. The author does not really collect feedback from human users but derives them from labeled data. One can imagine users can find out that returned answers are contrastive to commonsense. For instance, one can know that “Tokyo” is definit... | 3. The adopted baseline models are weak. First of all, the author does not compare to Campos et al. (2020), which also uses feedback in QA tasks. Second, they also do no comparison with other domain adaptation methods, such as those work cited in Section 8. Line 277: “The may be attributed…” -> “This may be attributed… |
ARR_2022_113_review | ARR_2022 | The methodology part is a little bit unclear. The author could describe clearly how the depth-first path completion really works using Figure 3. Also, I'm not sure if the ZIP algorithm is proposed by the authors and also confused about how the ZIP algorithm handles multiple sequence cases.
- Figure 2, it is not clear a... | - In figure 5, the y-axis label may use "Exact Match ratio" directly. |
ARR_2022_149_review | ARR_2022 | - The attribute-based approach can be useful if the attribute is given. This limits the application of the proposed approach if there is no attribute given but the text is implicitly offensive.
- It is not described if the knowledge bases that are inserted in are free from societal biases, or the issue is not affected ... | - It is not described if the knowledge bases that are inserted in are free from societal biases, or the issue is not affected by such restriction. Comments - I like attacking implicit offensive texts with reasoning chains, but not yet convinced with the example of Fig. |
ACL_2017_365_review | ACL_2017 | 1) Instead of arguing that the MTL approach replaces the attention mechanism, I think the authors should investigate why attention did not work on MTL, and perhaps modify the attention mechanism so that it would not harm performance.
2) I think the authors should reference past seq2seq MTL work, such as [2] and [3]. Th... | - it is always easier to show something (i.e. attention in seq2seq MTL) is not working, but the value would lie in finding out why it fails and changing the attention mechanism so that it works. |
ARR_2022_202_review | ARR_2022 | 1. The write-up has many typos and some formulas/explanations are confusing.
2. The technical innovation of the proposed method is limited. The proposed objective function is basically a combination of two related works with tiny changes.
3. Reproductivity is not ideal, as some essential parts are not addressed in the ... | 14. Table 3: MCNC should have many strong baselines that are not compared here, such as the baselines in [1]. Can you justify the reason? |
ARR_2022_303_review | ARR_2022 | - Citation type recognition is limited to two types –– dominant and reference –– which belies the complexity of the citation function, which is a significant line of research by other scholars. However this is more of a choice of the research team in limiting the scope of research.
- Relies on supplemental space to con... | - Relies on supplemental space to contain the paper. The paper is not truly independent given this problem (esp. S3.1 reference to Sup. Fig. 6) and again later as noted with the model comparison and other details of the span vs. sentence investigation. |
ACL_2017_71_review | ACL_2017 | -The explanation of methods in some paragraphs is too detailed and there is no mention of other work and it is repeated in the corresponding method sections, the authors committed to address this issue in the final version.
-README file for the dataset [Authors committed to add README file] - General Discussion: - Sect... | - In section 2.3 the authors use Lample et al. Bi-LSTM-CRF model, it might be beneficial to add that the input is word embeddings (similarly to Lample et al.) - Figure 3, KNs in source language or in English? ( since the mentions have been translated to English). In the authors' response, the authors stated that they w... |
ARR_2022_311_review | ARR_2022 | - The main weaknesses of the paper are the experiments, which is understandable for a short paper but I'd still expect it to be stronger. First, the setting is only on extremely low-resource regime, which is not the only case we want to use data augmentation in real-world applications. Also, sentence classification is ... | - The main weaknesses of the paper are the experiments, which is understandable for a short paper but I'd still expect it to be stronger. First, the setting is only on extremely low-resource regime, which is not the only case we want to use data augmentation in real-world applications. Also, sentence classification is ... |
ACL_2017_331_review | ACL_2017 | The document-independent crowdsourcing annotation is unreliable. - General Discussion: This work creates a new benchmark corpus for concept-map-based MDS. It is well organized and written clearly. The supplement materials are sufficient. I have two questions here.
1) Is it necessary to treat concept map extraction as a... | 1) Is it necessary to treat concept map extraction as a separate task? On the one hand, many generic summarization systems build a similar knowledge graph and then generate summaries accordingly. On the other hand, with the increase of the node number, the concept map becomes growing hard to distinguish. Thus, the gene... |
ARR_2022_112_review | ARR_2022 | - The paper does not discuss much about linguistic aspect of the dataset. While their procedures are thoroughly described, analyses are quite limited in that they do not reveal much about linguistic challenges in the dataset as compared to, for example, information extraction. The benefit of pretraining on the target d... | - Relating to the first point, authors should describe more about the traits of the experts and justify why annotation must be carried out by the experts, outside its commercial values. Were the experts linguistic experts or domain experts? Was annotation any different from what non-experts would do? Did it introduce a... |
ACL_2017_503_review | ACL_2017 | Reranking use is not mentioned in the introduction.
It would be a great news in NLP context if an Earley parser would run in linear time for NLP grammars (unlike special kinds of formal language grammars).
Unfortunately, this result involves deep assumptions about the grammar and the kind of input. Linear complexity of... | 1) Lines 102-106 is misleading. While intersection and probs are true, "such distribution" cannot refer to the discussion in the above. |
ARR_2022_233_review | ARR_2022 | Additional details regarding the creation of the dataset would be helpful to solve some doubts regarding its robustness. It is not stated whether the dataset will be publicly released.
1) Additional reference regarding explainable NLP Datasets: "Detecting and explaining unfairness in consumer contracts through memory n... | 1) Additional reference regarding explainable NLP Datasets: "Detecting and explaining unfairness in consumer contracts through memory networks" (Ruggeri et al 2021) |
ARR_2022_98_review | ARR_2022 | 1. Human evaluations were not performed. Given the weaknesses of SARI (Vásquez-Rodríguez et al. 2021) and FKGL (Tanprasert and Kauchak, 2021), the lack of human evaluations severely limits the potential impact of the results, combined with the variability in the results on different datasets.
2. While the authors expla... | 3. It will be nice to see some examples of the system on actual texts (vs. other components & models). |
ARR_2022_232_review | ARR_2022 | - A number of claims from this paper would benefit from more in-depth analysis.
- There are still some methodological flaws that should be addressed.
### Main questions/comments Looking at the attached dataset files, I cannot work out whether the data is noisy or if I don't understand the format. The 7th example in the... | - A number of claims from this paper would benefit from more in-depth analysis. |
ACL_2017_483_review | ACL_2017 | - 071: This formulation of argumentation mining is just one of several proposed subtask divisions, and this should be mentioned. For example, in [1], claims are detected and classified before any supporting evidence is detected.
Furthermore, [2] applied neural networks to this task, so it is inaccurate to say (as is cl... | - Two things must be improved in the presentation of the model: (1) What is the pooling method used for embedding features (line 397)? and (2) Equation (7) in line 472 is not clear enough: is E_i the random variable representing the *type* of AC i, or its *identity*? Both are supposedly modeled (the latter by feature r... |
ARR_2022_215_review | ARR_2022 | 1. The paper raises two hypotheses in lines 078-086 about multilinguality and country/language-specific bias. While I don't think the hypotheses are phrased optimally (could they be tested as given?), their underlying ideas are valuable. However, the paper actually does not really study these hypotheses (nor are they e... | 1. The paper raises two hypotheses in lines 078-086 about multilinguality and country/language-specific bias. While I don't think the hypotheses are phrased optimally (could they be tested as given?), their underlying ideas are valuable. However, the paper actually does not really study these hypotheses (nor are they e... |
ARR_2022_121_review | ARR_2022 | 1. The writing needs to be improved. Structurally, there should be a "Related Work" section which would inform the reader that this is where prior research has been done, as well as what differentiates the current work with earlier work. A clear separation between the "Introduction" and "Related Work" sections would ce... | 2. Would the use of feature engineering help in improving the performance? Uto et al. (2020)'s system reaches a QWK of 0.801 by using a set of hand-crafted features. Perhaps using Uto et al. (2020)'s same feature set could also improve the results of this work. |
ARR_2022_186_review | ARR_2022 | - it is not clear what's the goal of the paper. Is the release of a challenging dataset or proposing an analysis of augmenting models with expert guided adversarial examples. If it is the first, ok, but the paper misses a lot of important information, and data analysis to give a sense of the quality and usefulness of s... | - What is not clear also to me is how is used the Challenge Set. If I understood correctly, the CS is created by the linguistic experts and it's used for evaluation purposes. Is this used also to augment the training material? If yes, what is the data split you used? |
ACL_2017_128_review | ACL_2017 | ----- I'm not very convinced by the empirical results, mostly due to the lack of details of the baselines. Comments below are ranked by decreasing importance.
- The proposed model has two main parts: sentence embedding and substructure embedding. In Table 1, the baseline models are TreeRNN and DCNN, they are originally... | - The paper claims the model generalizes to different knowledge but I think the substructure has to be represented as a sequence of words, e.g. it doesn't seem straightforward for me to use constituent parse as knowledge here. Finally, I'm hesitating to call it "knowledge". This is misleading as usually it is used to r... |
ACL_2017_108_review | ACL_2017 | The problem itself is not really well motivated. Why is it important to detect China as an entity within the entity Bank of China, to stay with the example in the introduction? I do see a point for crossing entities but what is the use case for nested entities? This could be much more motivated to make the reader inter... | - first mention of multigraph: some readers may benefit if the notion of a multigraph would get a short description - previously noted by ... many previous: sounds a little odd - Solving this task: which one? |
ACL_2017_614_review | ACL_2017 | - I don't understand effectiveness of the multi-view clustering approach.
Almost all across the board, the paraphrase similarity view does significantly better than other views and their combination. What, then, do we learn about the usefulness of the other views? There is one empirical example of how the different vie... | - The relatively poor performance on nouns makes me uneasy. While I can expect TWSI to do really well due to its nature, the fact that the oracle GAP for PPDBClus is higher than most clustering approaches is disconcerting, and I would like to understand the gap better. This also directly contradicts the claim that the ... |
ACL_2017_108_review | ACL_2017 | Clarification is needed in several places.
1. In section 3, in addition to the description of the previous model, MH, you need point out the issues of MH which motivate you to propose a new model.
2. In section 4, I don't see the reason why separators are introduced. what additional info they convene beyond T/I/O?
3. s... | 4. the discussion in section 5.2 is so abstract that I don't get the insights why the new model is better than MH. can you provide examples of spurious structures? |
ARR_2022_114_review | ARR_2022 | By showing that there is an equivalent graph in the rank space on which message passing is equivalent to message passing in the original joint state and rank space, this work exposes the fact that these large structured prediction models with fully decomposable clique potentials (Chiu et al 2021 being an exception) are... | 1. For each PCFG with rank r, add a baseline smaller PCFG with state size being r, but where $H, I, J, K, L$ are directly parameterized as learned matrices of $\mathcal{R}^{r \times r}$, $\mathcal{R}^{r \times o}$, $\mathcal{R}^{r}$, etc. Under this setting, parsing F-1 might not be directly comparable, but perplexity ... |
ARR_2022_215_review | ARR_2022 | 1. The paper raises two hypotheses in lines 078-086 about multilinguality and country/language-specific bias. While I don't think the hypotheses are phrased optimally (could they be tested as given?), their underlying ideas are valuable. However, the paper actually does not really study these hypotheses (nor are they e... | - 241: It would also be good to state the maximum number of tasks done by any annotator. |
ACL_2017_489_review | ACL_2017 | 1) The main weakness for me is the statement of the specific hypothesis, within the general research line, that the paper is probing: I found it very confusing. As a result, it is also hard to make sense of the kind of feedback that the results give to the initial hypothesis, especially because there are a lot of them ... | 5) The paper contains many empirical results and analyses, and it makes a concerted effort to put them together; but I still found it difficult to get the whole picture: What is it exactly that the experiments in the paper tell us about the underlying research question in general, and the specific hypothesis tested in ... |
ACL_2017_792_review | ACL_2017 | 1. Unfortunately, the results are rather inconsistent and one is not left entirely convinced that the proposed models are better than the alternatives, especially given the added complexity. Negative results are fine, but there is insufficient analysis to learn from them. Moreover, no results are reported on the word a... | 4. What is the size of the training corpora? For instance, using different proportions of BabelWiki and SEW is shown in Figure 4; however, the comparison is somewhat problematic if the sizes are substantially different. The size of SemCor is moreover really small and one would typically not use such a small corpus for ... |
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