The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index; none of the 1 sources its claims cite is checked for support yet.
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Open accessAbstract
Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
PeerRead is introduced as the first public dataset of scientific peer reviews, containing 14.7K paper drafts with accept/reject decisions and 10.7K textual reviews from ACL, NIPS, ICLR, CoNLL, and arXiv. The paper analyzes review patterns and defines two NLP tasks: predicting paper acceptance and predicting review aspect scores.
Key findings
- PeerRead v1 contains 14,784 papers and 10,770 reviews across NIPS 2013–2017 (2,420 papers, 9,152 reviews), ICLR 2017 (427 papers, 1,304 reviews), ACL 2017 (137 papers, 275 reviews), CoNLL 2016 (22 papers, 39 reviews), and arXiv 2007–2017 (11,778 papers, no reviews).
- In ACL 2017 reviews, overall recommendation correlates most with substance (ρ = 0.59) and clarity (ρ = 0.42), and least with soundness/correctness (ρ = 0.01) and originality (ρ = 0.08).
- Reviews recommending oral presentation have a mean overall recommendation of 3.83 versus 2.92 for poster recommendations, a difference of 0.90 on a 1–5 scale.
- Acceptance classification outperforms majority baselines in all tested sections: ICLR 65.3% vs. 57.6%, arXiv cs.cl 75.7% vs. 68.9%, cs.lg 70.7% vs. 67.9%, and cs.ai 92.6% vs. 92.1%; the abstract reports up to 21% error reduction, while the results section states up to 22%.
- For aspect score prediction, the paper reports that the “Paper;Review” model outperforms the mean baseline in all four high-variance aspects examined, and the “Review” model outperforms the mean baseline in three out of four.
Methods
- Dataset construction used three strategies: opt-in collection with Softconf for CoNLL 2016 and ACL 2017; crawling publicly available NIPS 2013–2017 and ICLR 2017 OpenReview reviews; and crawling arXiv 2007–2017 submissions, labeling accepted papers via DBLP matching and title Levenshtein distance < 0.1, and labeling “probably-rejected” papers by venue, arXiv category, cross-listing, and submission-date criteria.
- Aspect scores were annotated for 1.3K ICLR 2017 reviews by two authors across seven aspects (appropriateness, clarity, originality, soundness/correctness, meaningful comparison, substance, impact) on a 1–5 scale, with a “not discussed” option; re-annotation of 30 reviews showed 77% consistent annotations, 2% inconsistent, and 21% “not discussed” mismatches.
- NLP experiments included binary acceptance classification using 22 coarse features plus lexical features with classifiers such as logistic regression, SVM, Random Forest, and MLP, and aspect score regression using CNN, LSTM, and DAN architectures with GloVe 840B embeddings, evaluated by RMSE against a mean-score baseline.
Limitations
- The paper states that ACL 2017 reviews were explicitly opted-in, likely biasing included ACL reviews toward higher quality, and that lower ICLR appropriateness scores are probably an annotation artifact because reviewers mention appropriateness mainly when a paper is inappropriate.
- The paper notes that NIPS provides reviews only for accepted papers, that some “probably-rejected” arXiv papers may have been published at workshops or other venues, and that one false negative was found due to a significant title change.
- The paper states its models are simple and intended to demonstrate dataset potential rather than be best-performing; evident from the text, arXiv rejection labels are heuristic “probably-rejected” rather than verified decisions, ICLR aspect scores are annotator-predicted rather than reviewer-provided, and aspect annotation covers only 1.3K reviews with many aspects often not discussed.
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Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
28 Own result · 13 No basis given · 6 Own method · 6 Own interpretation · 1 No full text of the sources
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reviews include both text and aspect scores (e.g., calrity) on a scale of 1-5.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.1 Review Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our best model outperforms the majority classifiers in all cases.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Another interesting observation is that the mean appropriateness score is lower in ICLR 2017 compared to ACL 2017.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The annotators were able to infer about half of the aspect scores from the corresponding review text (the other half was not discussed in the review text).”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In order to increase the utility of the dataset, we annotated 1.3K reviews with aspect scores, based on the corresponding review text.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This process results in 8,887 'probably-rejected' papers.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.1 Review Collection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The aspects which correlate most strongly with the final recommendation are substance (which concerns the amount of work rather than its quality) and clarity.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“a total of 1,304 official, anonymous reviews for 427 papers (177 accepted and 255 rejected).”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.1 Review Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We analyzed the dataset, showing interesting trends such as a high correlation between overall recommendation and recommending an oral presentation.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 6 Conclusion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.”
- Also stated in: § 1 Introduction · plain
“Our results show that we can predict the accept/reject decisions with 6-21% error reduction compared to the majority reject-all baseline, in four different sections of PeerRead.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1 Introduction · plain
- Probably not a substantial claim. 16
- It may state two separate claims as one. 14
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast, we collected and organized PeerRead such that it is easy for other researchers to use it for research purposes, replicate experiments and make a fair comparison to previous results.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 4.2 Review Aspect Score Prediction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This resulted in 39 reviews for 22 CoNLL 2016 submissions, and 275 reviews for 137 ACL 2017 submissions.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.1 Review Collection
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found 364 papers which matched these criteria, 352 out of which were labeled as 'accepted'.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.1 Review Collection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR.The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers.”
- Also stated in: § 1 Introduction · plain
“In total, the dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions, including a subset of 3K papers for which we have 10.7K textual reviews written by experts.”
- Also stated in: § 1 Introduction · plain
“total 14,784 10,770”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 1 Introduction · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most of the mean scores are similar in both sections, with a few notable exceptions.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We organize v1.0 of the PeerRead dataset in five sections: CoNLL 2016, ACL 2017, ICLR 2017, NIPS 2013-2017 and arXiv 2007-2017.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.3 Organization and Preprocessing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We introduced PeerRead, the first publicly available peer review dataset for research purposes, containing 14.7K papers and 10.7K reviews.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 6 Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The papers in each section are further split into standard training, development and test sets with 0.9:0.05:0.05ratios.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.3 Organization and Preprocessing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We defined two novel tasks based on PeerRead: (i) predicting the acceptance of a paper based on textual features and (ii) predicting the score of each aspect in a review based on the paper and review contents.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 6 Conclusion
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“arXiv 2007-2017 11,778 --2,891 / 8,887”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 1 Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On average, 77% of the annotations were consistent (i.e., the re-annotation was exactly the same as the original annotation, or was off by 1 point) and 2% were inconsistent (i.e., the re-annotation was off by 2 points or more).”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We defined two novel tasks based on PeerRead: (i) predicting the acceptance of a paper based on textual features and (ii) predicting the score of each aspect in a review based on the paper and review contents.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 6 Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, about 49% of the reviews discussed the 'originality' aspect, while only 5% discussed 'appropriateness'.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our best model outperforms the majority classifier in all cases, with up to 22% error reduction.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > Experimental setup.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While aspect scores assigned by reviewers are included in the opted-in sections in PeerRead, they are missing from the remaining reviews.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CoNLL 2016 section also includes aspect scores but is too small for training.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Note that NIPS provide reviews only for accepted papers.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 1 Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Presentation format Oral Poster ∆ stdev Recommendation 3.83 2.92 0.90 0.89 Substance 3.91 3.29 0.62 0.84 Clarity 4.19 3.72 0.47 0.90 Meaningful comparison 3.60 3.36 0.24 0.82 Impact 3.27 3.09 0.18 0.54 Originality 3.91 3.88 0.02 0.87 Soundness/Correctness 3.93 4.18 -0.25 0.91”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The CoNLL 2016 section also includes aspect scores but is too small for training.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The platform also allows any person to review the paper by adding a comment, but we only use the official reviews of reviewers assigned to review that paper.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This baseline is guaranteed to obtain mean square errors less than or equal to the majority baseline.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In order to assess annotation consistency, the same annotators re-annotated a random sample consisting of 30 reviews.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For consistency, we only include the first arXiv version of each paper (accepted or rejected) in the dataset.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“If a paper has multiple versions, we consider the submission date of the first version.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note, however, that ACL 2017 reviews were explicitly opted-in while the ICLR 2017 reviews include all official reviews, which is likely to result in a positive bias in review quality of the ACL reviews included in this study.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 2.4 Aspect Score Annotations
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that some of the 'probably-rejected' papers may be published at workshops or other venues.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Importantly, our annotators only considered the review text, and did not have access to the papers.”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > B Reviewer Instructions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“For example, the organizers of the NIPS 2014 conference assigned 10% of conference submissions to two different sets of reviewers to measure the consistency of the peer reviewing process, and observed that the two committees disagreed on the accept/reject decision for more than a quarter of the papers (Langford and Guzdial, 2015).”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > 1 Introduction
What the sources say
- Not checked — no full text of the sourcecites Langford and Guzdial, 2015 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
Quantitative studies of peer reviews had been limited, for the most part, to the few individuals who had access to peer reviews of a given venue.
“Despite these efforts, quantitative studies of peer reviews had been limited, for the most part, to the few individuals who had access to peer reviews of a given venue (e.g., journal editors and program chairs).”
The paper relies on this without support.
Most peer reviews are not made publicly available.
“Surprisingly, however, most of the reviews are not made publicly available.”
The paper relies on this without support.
Review length is often thought of as a measure of review quality.
“Since review length is often thought of as a measure of its quality, this raises interesting questions about the quality of reviews in ICLR vs. ACL conferences.”
The paper relies on this without support.
In many publication venues, reviewers assign numeric aspect scores as part of the peer review.
“In many publication venues, reviewers assign numeric aspect scores (e.g., clarity, originality, substance) as part of the peer review.”
The paper relies on this without support.
The presentation format decision of accepted papers is based on recommendation by the reviewers.
“The presentation format decision of accepted papers is based on recommendation by the reviewers.”
The paper relies on this without support.
The baseline models lack the sophistication to assess the quality of the work discussed in the given paper.
“Since our models lack the sophistication to assess the quality of the work discussed in the given paper, this might indicate that some of the features we define are correlated with strong papers, or bias reviewers' judgments.”
The paper relies on this without support.
Aspects with higher RMSE scores for the 'Mean' baseline indicate higher variance among the review scores for this aspect.
“We note that aspects with higher RMSE scores for the 'Mean' baseline indicate higher variance among the review scores for this aspect, so we focus our discussion on these aspects.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Despite the fact that this dataset was supposed to be released periodically, the last update available in its GitHub repositoryfoot_4 dates back to May 2018.The content is currently limited, based on a few conferences before 2018.”
From The Open Review-Based (ORB) dataset: Towards Automatic Assessment of Scientific Papers and Experiment Proposals in High-Energy Physics (2023) · cites this paper as [Kang et al., 2018]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“PeerRead (Kang et al., 2018), MOPRD (Lin et al., 2023b), and NLPeer (Dycke et al., 2023) are datasets containing a large number of peer reviews and their corresponding papers but without expert annotations.”
From LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024) · cites this paper as Kang et al., 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Science Parse (Sci, 2017-2025) Kang et al. (2018) PDF → JSON Fast metadata + rough text; webscale.”
From Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026) · cites this paper as Kang et al. (2018)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“existing evaluations of AI-assisted peer review mainly focus on the overall quality of generated reviews or the accuracy of final decisions.”
From Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review (2026) · cites this paper as Kang et al. 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“First, existing peer-review datasets usually contain heterogeneous paper-review-decision records.Their field structures, text granularity, and label sources differ”
From Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review (2026) · cites this paper as Kang et al. 2018
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Few studies compare process-variable decision value, evaluate chain consis-tency, and examine the interventional sensitivity of final decisions to intermediate process variables within a unified framework.”
From Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review (2026) · cites this paper as Kang et al. 2018
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Empirical analyses show that reviewer comments are distributed across methodology, theory, writing, originality, impact, clarity, soundness, and recommendation rather than concentrated in a single type of problem (Dycke et al., 2023;Kang et al., 2018;Stephen, 2022).”
From Beyond Human-Likeness: Mapping the Scientific Critique Profiles of LLMs and Human Reviewers (2026) · cites this paper as Kang et al., 2018
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Review score prediction has been first introduced in (Kang et al., 2018) and further explored in (Ghosal et al., 2019).”
From NLPeer: A Unified Resource for the Computational Study of Peer Review (2023) · cites this paper as Kang et al., 2018
The cited source says what the claim attributes to it.
In the citing paper
“Parts of the PeerRead data (Kang et al., 2018) have been created with explicit consent for publication and processing by both reviewers and authors, and we include them into our resource.”
From NLPeer: A Unified Resource for the Computational Study of Peer Review (2023) · cites this paper as Kang et al., 2018
The cited source says what the claim attributes to it.
In the citing paper
“Early approaches in this domain were confined to sub-tasks, such as predicting paper acceptance from metadata and abstract features or regressing review scores from paper content (Kang et al., 2018;Checco et al., 2021).”
From Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026) · cites this paper as Kang et al., 2018
The cited source says what the claim attributes to it.
In the citing paper
“These foundational efforts deconstructed the peer review process into analytical (Teufel et al., 1999), predictive (Kang et al., 2018), and narrowly generative components (Dycke et al., 2023b).”
From Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026) · cites this paper as Kang et al., 2018
The cited source says what the claim attributes to it.
In the citing paper
“These datasets cover an early peer-review corpus, the ACL Rolling Review process, and public ICLR review records, respectively, and differ in field structure, text granularity, and label sources.”
From Beyond Final Decisions: A Process-Centric Benchmark for Transparent AI-Assisted Peer Review (2026) · cites this paper as Kang et al. 2018
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, although the above works have made progress in scholarly peer review, most of these datasets could not guarantee the consistency between their paper content and those under review, nor did they include rebuttals, making it difficult to support the research about the reviewers-authors discussions.”
From Re2: A Consistency-ensured Dataset for Full-stage Peer Review and Multi-turn Rebuttal Discussions (2025) · cites this paper as Kang et al. [2018]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > Abstract
“up to 21% error reduction compared to the majority baseline”
§ A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications > Experimental setup.
“Our best model outperforms the majority classifier in all cases, with up to 22% error reduction.”
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MethodScikit-learn: Machine Learning in Python
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MethodGlove: Global Vectors for Word Representation
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MethodDeep Unordered Composition Rivals Syntactic Methods for Text Classification
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MethodOpen peer review: a randomised controlled trial
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BackgroundContent-Based Citation Recommendation
Bhagavatula, C., Feldman, S., Power, R., & Ammar, W. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1022 · Cited by 188 · cited 2×
“Tools like Citeomatic 29 (Bhagavatula et al., 2018) are especially useful in finding relevant articles not cited in the manuscript.”
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Cited by
Sorted by citations
Rogers, A., Boyd-Graber, J., & Okazaki, N. (2023) · doi:10.18653/v1/2023.acl-short · Cited by 208
A context-aware citation recommendation model with BERT and graph convolutional networks
Jeong, C., Jang, S., Park, E., & Choi, S. (2020) · Scientometrics · doi:10.1007/s11192-020-03561-y · Cited by 186
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Wu, J., Yang, S., Zhan, R., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Computational Linguistics · doi:10.1162/coli_a_00549 · Cited by 212
Argument Mining for Understanding Peer Reviews
Hua, X., Nikolov, M., Badugu, N., & Wang, L. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1219 · Cited by 86
Can We Automate Scientific Reviewing?
Yuan, W., Liu, P., & Neubig, G. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12862 · Cited by 96
Peer review analyze: A novel benchmark resource for computational analysis of peer reviews
Ghosal, T., Kumar, S., Bharti, P. K., & Ekbal, A. (2022) · PLOS ONE · doi:10.1371/journal.pone.0259238 · Cited by 66
“For definitions of each aspect-label, please follow the appendix section in [49].”
InfluentialDeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions
Ghosal, T., Verma, R., Ekbal, A., & Bhattacharyya, P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1106 · Cited by 62
“Training is done with ICLR 2017+ICLR 2018 papers/reviews, † → Cross-Domain: Training on ICLR and testing upon the entire data of ACL/CoNLL, ‡Test Set is kept the same as (Kang et al., 2018), RMSE→Root Mean Squared Error, ∗ →65.”
BackgroundMethodResultInfluentialAPE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning
Cheng, L., Bing, L., Yu, Q., Lu, W., & Si, L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.569 · Cited by 58
Intrinsic Evaluation of Summarization Datasets
Bommasani, R., & Cardie, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.649 · Cited by 60
“PeerRead (Kang et al., 2018) is a dataset of paper drafts from top-tier computer science venues as well as arXiv.”
MethodDoes My Rebuttal Matter? Insights from a Major NLP Conference
Gao, Y., Eger, S., Kuznetsov, I., Gurevych, I., & Miyao, Y. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1129 · Cited by 48
“In line with Kang et al. (2018), we first assess the impact of individual aspect scores on the overall score by measuring their Pearson correlation, illustrated in Fig.”
BackgroundMethodResultInfluentialA Neural Citation Count Prediction Model based on Peer Review Text
Li, S., Zhao, W. X., Yin, E. J., & Wen, J.-R. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1497 · Cited by 45
“The data of NIPS 2013-2016 and ICLR 2017 have been shared by Kang et al. (2018).”
BackgroundMethodThelwall, M. A. (2024) · Journal of Data and Information Science · doi:10.2478/jdis-2025-0011 · Cited by 54
The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing
Zhou, R., Awasthi, A., & Cardinal, J. S.-L. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103369 · Cited by 41
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations
Chen, M., Chu, Z., & Gimpel, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1060 · Cited by 29
“We construct the dataset from PeerRead (Kang et al., 2018), which consists of scientific papers from a variety of fields.”
MethodReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
Wang, Q., Zeng, Q., Huang, L., Knight, K., Ji, H., & Rajani, N. F. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.44 · Cited by 38
“We follow the data split of PeerRead (Kang et al., 2018) 6 .”
InfluentialXue, Z., He, G., Liu, J., Jiang, Z., Zhao, S. X., & Lu, W. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103216 · Cited by 34
(2020) · doi:10.18653/v1/2020.findings-emnlp.112 · Cited by 32
“…revealed only four conference papers on peer review from a meta-research perspective: a paper-reviewer matching tool (Anjum et al., 2019), a corpus of reviews (Kang et al., 2018), and two experimental studies using NLP to explain the observed reviews (Caragea et al., 2019; Gao et al., 2019).”
MethodThelwall, M. A., & Yaghi, A. (2025) · Scientometrics · doi:10.1007/s11192-025-05287-1 · Cited by 36
“For this study, metadata and reviewer scores for ICLR submissions were extracted from a repository curated for research purposes (Kang et al., 2018).”
Predicting article quality scores with machine learning: The U.K. Research Excellence Framework
Thelwall, M. A., Kousha, K., Wilson, P., Makita, M., Abdoli, M., Stuart, E., Levitt, J. M., Knoth, P., & Cancellieri, M. (2023) · Quantitative Science Studies · doi:10.1162/qss_a_00258 · Cited by 35
ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing
Liu, R., & Shah, N. B. (2023) · arXiv · doi:10.48550/arxiv.2306.00622 · Cited by 23
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
Jung, T., Kang, D., Mentch, L., & Hovy, E. H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1327 · Cited by 30
Self-citationMReD: A Meta-Review Dataset for Structure-Controllable Text Generation
Shen, C., Cheng, L., Zhou, R., Bing, L., You, Y., & Si, L. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.198 · Cited by 23
“Researchers recently explore the peer review domain data for a few tasks, such as PeerRead (Kang et al., 2018) for paper decision predictions, AM-PERE (Hua et al., 2019) for proposition classification in reviews, and RR (Cheng et al., 2020) for paired-argument extraction from review-rebuttal pairs.”
BackgroundUncertainty Estimation and Reduction of Pre-trained Models for Text Regression
Wang, Y., Beck, D., Baldwin, T., & Verspoor, K. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00483 · Cited by 23
“For SA, we use: (1) a large-scale product review dataset, Yelp (Sabnis, 2018); and (2) a small paper review rating dataset, PeerRead (Kang et al., 2018), augmented with 399 Spanish paper reviews (Keith et al., 2017) machine-translated into English.”
MethodInfluentialLLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Du, J., Wang, Y., Zhao, W., Deng, Z., Liu, S., Lou, R., Zou, H. P., Narayanan Venkit, P., Zhang, N., Srinath, M., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.292 · Cited by 23
Defining quality in peer review reports: a scoping review
Sizo, A., Lino, A. D. P., Rocha, Á., & Reis, L. P. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02435-0 · Cited by 26
Wu, W., Zhang, C., & Zhao, Y. (2025) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.70005 · Cited by 32
“Kang et al. (2018) were pioneers in introducing the first large-scale peer-review dataset, PeerRead, comprising full-text papers from ACL, ICLR, and NIPS, along with the corresponding editorial decisions of acceptance or rejection.”
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
Wenniger, G. M. D. B., van Dongen, T., Aedmaa, E., Kruitbosch, H., Valentijn, E. A., & Schomaker, L. (2020) · Workshop on Scholarly Document Processing (SDP) · doi:10.18653/v1/2020.sdp-1.18 · Cited by 31
“arXiv sub-domain dataset Majority class prediction Benchmark (Kang et al., 2018) BiLSTM (Shen et al.”
BackgroundMethodResultInfluentialCSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Lin, H., Ma, L., Zhu, J., Xiang, L., Zhou, Y., Zhang, J., & Zong, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.365 · Cited by 37
“Recently, there exist various types of document summarization datasets such as news reports (Nallapati et al., 2016; Narayan et al., 2018), wiki passages (Liu et al., 2018), and scientific papers (Kang et al., 2018).”
BackgroundA Joint Model for Multimodal Document Quality Assessment
Shen, A., Salehi, B., Baldwin, T., & Qi, J. (2019) · 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) · doi:10.1109/jcdl.2019.00024 · Cited by 24
“…FA indicator, which is a bronze star icon in the top right corner of the webpage) are removed from the screenshot. arXiv dataset The arXiv dataset (Kang et al. 2018) consists of three subsets of academic articles under the arXiv repository of Computer Science (cs), from the three subject areas…”
InfluentialA Sentiment Augmented Deep Architecture to Predict Peer Review Outcomes
Ghosal, T., Verma, R., Ekbal, A., & Bhattacharyya, P. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00096 · Cited by 19
“Table 2: Results on Aspect Score Prediction and Accept/Reject Classification Tasks, Task-1 (score prediction) training is done with only ICLR 2017 papers/reviews while Task 2 (classification) training is done with ICLR 2017+ICLR 2018 papers/reviews, † → Cross-Domain: Training on ICLR and Testing upon entire data of AC…”
MethodResultInfluentialTransferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts
Accuosto, P., & Saggion, H. (2019) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/w19-4505 · Cited by 24
“We also intend to expand our acceptance prediction experiments using the PeerRead dataset (Kang et al., 2018), 21 which has a greater coverage than the NIPS and ICLR subsets that we used in our experiments.”
MethodBreaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
Ravichander, A., Black, A. W., Norton, T. B., Wilson, S., & Sadeh, N. (2021) · doi:10.18653/v1/2021.acl-long.319 · Cited by 27
“NLP has made inroads in digesting large amounts of text in domains such as scientific publications and news (Jain et al., 2020; Cachola et al., 2020; Kang et al., 2018; Rush et al., 2015; See et al., 2017), with several”
BackgroundMethodNLPeer: A Unified Resource for the Computational Study of Peer Review
Dycke, N., Kuznetsov, I., & Gurevych, I. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.277 · Cited by 50
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation
Deng, Z., Peng, H., Xia, C., Li, J., He, L., & Yu, P. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.555 · Cited by 19
“Kang et al. (2018) collected a dataset of peer reviews from several conferences and predicted paper acceptance decision by using paper draft.”
BackgroundThe influence of opening up peer review on the citations of journal articles
Ni, J., Zhao, Z.-Y., Shao, Y., Liu, S., Li, W., Zhuang, Y., Qu, J.-M., Cao, Y., Lian, N., & Li, J. (2021) · Scientometrics · doi:10.1007/s11192-021-04182-9 · Cited by 19
“The linguistic characteristics of the comments and responses are sometimes linked to ratings, citations, etc. Kang et al. (2018) built Natural Language Processing (NLP) models based on their database to predict whether the paper was going to be accepted.”
BackgroundInfluentialM4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Whitehouse, C., Afzal, O. M., Mahmoud, T., Sasaki, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.14902 · Cited by 19
“PeerRead Reviews We sampled 586 academic papers in fields of NLP and machine learning top-tier conferences released by ReerRead corpus (Kang et al., 2018), with metadata including title, abstract, and multiple human reviews for each paper.”
BackgroundMethodInfluentialKumar, S., Ghosal, T., Bharti, P. K., & Ekbal, A. (2021) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl52503.2021.00081 · Cited by 20
Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review
Kuznetsov, I., Buchmann, J., Eichler, M., & Gurevych, I. (2022) · Computational Linguistics · doi:10.1162/coli_a_00455 · Cited by 17
Bharti, P. K., Ghosal, T., Agarwal, M., & Ekbal, A. (2023) · International Journal on Digital Libraries · doi:10.1007/s00799-023-00375-0 · Cited by 20
“Mention could be made of the PeerRead [10] dataset.”
InfluentialWang, Z., Zhang, H., Chen, H., Feng, Y., & Ding, J. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102119 · Cited by 18
“These features can not only be obtained in advance, but also play a crucial role in identifying the quality of the paper (Kang et al., 2018; Checco et al., 2021; Kousha and Thelwall, 2023).”
InfluentialDoes My Rebuttal Matter? Insights from a Major NLP Conference
Gao, Y., Eger, S., Kuznetsov, I., Gurevych, I., & Miyao, Y. (2019) · TUbilio (Technical University of Darmstadt) · doi:10.48550/arxiv.1903.11367 · Cited by 16
“In line with Kang et al. (2018), we first assess the impact of individual aspect scores on the overall score by measuring their Pearson correlation, illustrated in Fig.”
BackgroundMethodResultInfluentialUncertainty-Aware Machine Support for Paper Reviewing on the Interspeech 2019 Submission Corpus
Stappen, L., Rizos, G., Hasan, M., Hain, T., & Schuller, B. W. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-2862 · Cited by 15
“The scores are predictable to a degree, in accordance to [6], as the RMSE and MAE values are between 0.”
BackgroundMethodInfluentialMachine learning approach to predicting the acceptance of academic papers
Skorikov, M., & Momen, S. (2020) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict50021.2020.9172011 · Cited by 15
“The data is taken from the PeerRead [11] dataset, which consists of 14.7K machine learning paper drafts and corresponding accept and reject decisions from top-tier venues.”
BackgroundMethodLarge language models in peer review: challenges and opportunities
Sun, Z. (2025) · Scientometrics · doi:10.1007/s11192-025-05440-w · Cited by 15
“For example, PeerRead contains reviews from machine learning conferences (Kang et al., 2018); NLPeer focuses on computational linguistics venues (Dycke et al., 2023); and Openreviewer offers a larger and more recent collection of human reviews from leading machine learning conferences (Szumega et…”
Detecting LLM-generated peer reviews
Rao, V. S., Kumar, A., Lakkaraju, H., & Shah, N. B. (2025) · PLOS One · doi:10.1371/journal.pone.0331871 · Cited by 14
Hug, S. E. (2024) · Scientometrics · doi:10.1007/s11192-023-04915-y · Cited by 15
“According to the estimates in Table 2, all evaluation criteria are positively related to the overall judgment, suggesting that referees use a broad range of criteria to evaluate an application and integrate the criteria in a linear, additive way.”
PEERAssist: Leveraging on Paper-Review Interactions to Predict Peer Review Decisions
Bharti, P. K., Ranjan, S., Ghosal, T., Agrawal, M., & Ekbal, A. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-91669-5_33 · Cited by 16
ER-AE: Differentially Private Text Generation for Authorship Anonymization
Bo, H., Ding, S. H. H., Fung, B. C. M., & Iqbal, F. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.314 · Cited by 30
“• Academic Review Dataset: All the public reviews from NeurIPS (2013-2018) and ICLR (2017) based on the original data and the web crawler provided by [21].”
BackgroundCREPE: Open-Domain Question Answering with False Presuppositions
Yu, X., Min, S., Zettlemoyer, L., & Hajishirzi, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.583 · Cited by 35
Wu, W., Xi, H., & Zhang, C. (2024) · Scientometrics · doi:10.1007/s11192-024-05070-8 · Cited by 12
“Among them, Kang et al. [15] introduced the first publicly available dataset, PeerRead, designed for scientific peer review research.”
A General Approach to Multimodal Document Quality Assessment
Shen, A., Salehi, B., Qi, J., & Baldwin, T. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11647 · Cited by 12
Qiao, F., Xu, L., & Han, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02934-0_7 · Cited by 13
Multi-task Peer-Review Score Prediction
Li, J., Sato, A., Shimura, K., & Fukumoto, F. (2020) · Workshop on Scholarly Document Processing (SDP) · doi:10.18653/v1/2020.sdp-1.14 · Cited by 15
“We use the ICLR and ACL datasets in the Peer-Read Dataset (Kang et al., 2018) because they provide the scores of the peer-review aspects.”
BackgroundMethodInfluentialMOPRD: A multidisciplinary open peer review dataset
Lin, J., Song, J., Zhou, Z., Chen, Y., & Shi, X. (2022) · arXiv · doi:10.48550/arxiv.2212.04972 · Cited by 11
“MOPRD is composed of paper metadata, manuscripts of the initial submission and following revisions, review comments, meta-re-views, author’s rebuttal letters, and editorial decisions of papers across various disciplines.”
BackgroundInfluentialA Hybrid Citation Recommendation Model With SciBERT and GraphSAGE
Dinh, T. N., Pham, P., Giang, N. L., Nguyên, N. T., & Vo, B. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3490774 · Cited by 13
Pendyala, V. S., Kamdar, K., & Mulchandani, K. (2025) · Electronics · doi:10.3390/electronics14020256 · Cited by 15
Back to the Future – Temporal Adaptation of Text Representations
Bjerva, J., Kouw, W. M., & Augenstein, I. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6240 · Cited by 16
A Deep Multimodal Investigation To Determine the Appropriateness of Scholarly Submissions
Ghosal, T., Raj, A., Ekbal, A., Saha, S., & Bhattacharyya, P. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00039 · Cited by 11
PolitePEER: does peer review hurt? A dataset to gauge politeness intensity in the peer reviews
Bharti, P. K., Navlakha, M., Agarwal, M., & Ekbal, A. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09662-3 · Cited by 12
“Kang et al. (2018) introduced PeerRead, which is the first large-scale peer review dataset.”
Li, Y.-X., Kou, G., Peng, Y., & Morente-Molinera, J. A. (2024) · Applied intelligence (Boston) · doi:10.1007/s10489-024-05765-8 · Cited by 8
KID-Review: Knowledge-Guided Scientific Review Generation with Oracle Pre-training
Yuan, W., & Liu, P. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21418 · Cited by 10
“…analysis (Tomkins, Zhang, and Heavlin 2017; Stelmakh, Shah, and Singh 2019), aspect-based sentiment analysis (Chakraborty, Goyal, and Mukher-jee 2020), decision classification (Kang et al. 2018; Qiao, Xu, and Han 2018), automatic review generation (Wang et al. 2020; Yuan, Liu, and Neubig 2021).”
BackgroundMethodHeterogeneous Supervised Topic Models
Sridhar, D., Daumé, H., & Blei, D. M. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00487 · Cited by 9
“This dataset comes from PeerRead, (Kang et al., 2018), a corpus of computer-science papers.”
MethodarXivEdits: Understanding the Human Revision Process in Scientific Writing
Jiang, C., Xu, W., & Stevens, S. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.641 · Cited by 18
Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and Beyond
Dycke, N., Kuznetsov, I., & Gurevych, I. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.23 · Cited by 10
“The ACL-2017 portion of the PeerRead corpus (Kang et al., 2018) includes reviews of submissions to the 55th Annual Meeting of ACL, for which both authors and reviewers agreed to share the review reports.”
BackgroundInfluentialA Neural Local Coherence Analysis Model for Clarity Text Scoring
Muangkammuen, P., Xu, S., Fukumoto, F., Saikaew, K. R., & Li, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.194 · Cited by 13
“The PeerRead dataset (Kang et al., 2018) was selected as a benchmark.”
MethodInfluentialOpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews
Idahl, M., & Ahmadi, Z. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations) · doi:10.18653/v1/2025.naacl-demo.44 · Cited by 10
Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs
Kang, D., & Hovy, E. H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1589 · Cited by 10
Self-citationOn Positivity Bias in Negative Reviews
Aithal, M., & Tan, C. (2021) · doi:10.18653/v1/2021.acl-short.39 · Cited by 12
“• PeerRead (Kang et al., 2018).”
Modeling Interference for Individual Treatment Effect Estimation from Networked Observational Data
Huang, Q., Ma, J., Li, J., Guo, R., Sun, H., & Chang, Y. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3628449 · Cited by 11
“PeerRead [27] is a dataset of computer scientific peer reviews for papers.”
BackgroundWhat Makes a Scientific Paper be Accepted for Publication?
Fytas, P., Rizos, G., & Specia, L. (2021) · doi:10.18653/v1/2021.cinlp-1.4 · Cited by 13
“PeerRead suffers from various data quality issues, which we have resolved.”
BackgroundMethodResultInfluentialAcceptance Decision Prediction in Peer-Review Through Sentiment Analysis
Ribeiro, A. C., Sizo, A., Cardoso, H. L., & Reis, L. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86230-5_60 · Cited by 10
What have we learned from OpenReview?
Wang, G., Peng, Q., Zhang, Y., & Zhang, M. (2022) · World Wide Web · doi:10.1007/s11280-022-01109-z · Cited by 9
“[13] predict the acceptance of a paper based on textual features and the score of each aspect in a review based on the paper and review contents.”
BackgroundCharacterizing Peer Review Comments of Academic Articles in Multiple Rounds
Han, R., Zhou, H., Zhong, J., & Zhang, C.-Z. (2022) · ASIS&T Annual Meeting · doi:10.1002/pra2.607 · Cited by 8
“Kang et al. (2018) defined two novel tasks based on PeerRead: (i) predicting the acceptance of a paper based on textual features and (ii) predicting the score of each aspect in a review based on the paper and review contents.”
MethodSentBS: Sentence-level Beam Search for Controllable Summarization
Shen, C., Cheng, L., Bing, L., You, Y., & Si, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.699 · Cited by 10
Exploring the Potential of GPT-2 for Generating Fake Reviews of Research Papers
Bartoli, A., & Medvet, E. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200717 · Cited by 12
Cognitive Representation Learning of Self-Media Online Article Quality
Wang, Y., Huang, S., Li, G., Deng, Q., Liao, D., Si, P., Yang, Y., & Xu, J. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413747 · Cited by 10
“[10] predicted whether an academic paper is accepted or rejected based on features such as the title length and whether specific words appear in the abstract.”
BackgroundLeveraging peer-review aspects for extractive and abstractive summarization of scientific articles
Majadly, M., & Last, M. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00665-z · Cited by 8
“While some datasets for sci-entific peer reviews exist, such as PeerRead [42], which is well-known and aids researchers in studying this topic, they are not directly applicable to the task of aspect extraction.”
Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers
Gîfu, D., & Silviu-Vasile, C. (2025) · Future Internet · doi:10.3390/fi17010038 · Cited by 10
“…(https://paperswithcode.com/datasets, accessed on 22 July 2024): This dataset contains human-written text from sources such as Wikipedia, WikiHow [45], Reddit (ELI5), arXiv, and PeerRead [46] for English, as well as news articles for Urdu, RuATD [47] for Russian, and Indonesian news articles.”
Kumar, S., Arora, H., Ghosal, T., & Ekbal, A. (2022) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3529372.3530937 · Cited by 8
“The first public dataset of scientific peer reviews was presented in [7] available for research purposes, providing an opportunity to study this important artifact.”
BackgroundER-AE: Differentially Private Text Generation for Authorship AnonymizationPreprint
Bo, H., Ding, S. H. H., Fung, B. C. M., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08736 · Cited by 7
“• Academic Review Dataset: All the public reviews from NeurIPS (2013-2018) and ICLR (2017) based on the original data and the web crawler provided by [21].”
BackgroundBharti, P. K., Agarwal, M., & Ekbal, A. (2024) · Scientometrics · doi:10.1007/s11192-024-04938-z · Cited by 6
“Kang et al. (2018) introduced PeerRead, the first large-scale peer review dataset, which contains full-text papers from ACL, ICLR, and NIPS along with their editorial accept/reject decisions and review comments.”
Basuki, S., & Tsuchiya, M. (2022) · IEEE Access · doi:10.1109/access.2022.3225871 · Cited by 6
“In this study area, the dataset published by [22] is the most cited work, which [22] compiled numerous peer-review datasets comprising ICLR, arXiv, Association for Computational Linguistics (ACL), and Conference on Computational Natural Language Learning (CoNLL).”
MethodReAct: A Review Comment Dataset for Actionability (and more)
Choudhary, G., Modani, N., & Maurya, N. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-91560-5_24 · Cited by 6
“[6] also present PeerRead dataset consolidating reviews from a lot of conferences.”
BackgroundKumar, A., Ghosal, T., Bhattacharjee, S., & Ekbal, A. (2023) · International Journal on Digital Libraries · doi:10.1007/s00799-023-00359-0 · Cited by 6
“The PeerRead [10] dataset is the first publicly available peer-review resource that encouraged Natural Language Processing (NLP)/ Machine Learning (ML) research on peer review problems.”
COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews
Singh, S., Singh, M., & Goyal, P. (2021) · 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) · doi:10.1109/jcdl52503.2021.00068 · Cited by 7
“Several works focus on curation of peer review data [3], and further analysis such as sentiment analysis of reviews and decision prediction [3, 4].”
BackgroundSentiment as an Ordinal Latent Variable
Stoehr, N., Cotterell, R., & Schein, A. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.8 · Cited by 7
“These are PeerRead (Kang et al., 2018), specifically the splits ACL and ICLR, IMDB (Maas et al.”
MethodArgument Mining for Understanding Peer ReviewsPreprint
Hua, X., Nikolov, M., Badugu, N., & Wang, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10104 · Cited by 14
What Can Natural Language Processing Do for Peer Review?
Kuznetsov, I., Afzal, O. M., Dercksen, K., Dycke, N., Goldberg, A., Hope, T., Hovy, D., Kummerfeld, J. K., Lauscher, A., Leyton-Brown, K., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.06563 · Cited by 6
Self-citationLin, T.-L., Chen, W.-C., Hsiao, T.-F., Liu, H.-I., Yeh, Y.-H., Chan, Y.-K., Lien, W.-S., Kuo, P.-Y., Yu, P. S., & Shuai, H.-H. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025 · doi:10.18653/v1/2025.findings-emnlp.259 · Cited by 6
An Analysis of Tasks and Datasets in Peer Reviewing
Staudinger, M., Kusa, W., Piroi, F., & Hanbury, A. (2024) · Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) · doi:10.18653/v1/2024.sdp-1.24 · Cited by 6
When Reviewers Lock Horns: Finding Disagreements in Scientific Peer Reviews
Kumar, S., Ghosal, T., & Ekbal, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1038 · Cited by 10
Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction
Muangkammuen, P., Fukumoto, F., Li, J., & Suzuki, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.164 · Cited by 5
“1 https://github.com/panitan-m/gamma_trans Much of the previous work on PASP is based on supervised learning (Kang et al., 2018; Li et al., 2020).”
BackgroundMethodInfluentialGORC: A large contextual citation graph of academic papersPreprint
Lo, K., Wang, L. L., Neumann, M. E., Kinney, R., & Weld, D. S. (2019) · arXiv (Cornell University) · Cited by 5
Large language models for peer review in biotechnology
Li, W., Zhao, R., Sun, P.-T., Lee, J., Lu, O., Yuan, S., Sureshkumar, P., Chen, Y., Xiao, Z., & Tang, Y. J. (2026) · New Biotechnology · doi:10.1016/j.nbt.2026.03.007 · Cited by 4
Heterogeneous hypergraph learning for literature retrieval based on citation intents
Shi, K., Liu, K., & He, X. (2024) · Scientometrics · doi:10.1007/s11192-024-05066-4 · Cited by 5
“The Peerread CIG has fewer links between the nodes and is a sparse graph, while the arXiv CIG is dense.”
InfluentialQuantitative Discourse Cohesion Analysis of Scientific Scholarly Texts Using Multilayer Networks
Bhatnagar, V., Duari, S., & Gupta, S. (2022) · IEEE Access · doi:10.1109/access.2022.3198952 · Cited by 7
“The first collection comprises 172 articles accepted in ICLR 2017 and is a part of the PeerRead collection (Kang et al., 2018).”
BackgroundInfluentialOverview of PragTag-2023: Low-Resource Multi-Domain Pragmatic Tagging of Peer Reviews
Dycke, N., Kuznetsov, I., & Gurevych, I. (2023) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/2023.argmining-1.21 · Cited by 6
“Yet, to date, NLP for peer review has focused on data from machine learning conferences (Kang et al., 2018; Hua et al., 2019; Cheng et al., 2020; Kennard et al., 2022), and the applications outside of this domain remain under-investigated.”
BackgroundInfluentialClaimBrush: A Novel Framework for Automated Patent Claim Refinement Based on Large Language Models
Kawano, S., Nonaka, H., & Yoshino, K. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825172 · Cited by 4
Multi-task Neural Shared Structure Search: A Study Based on Text Mining
Li, J., & Fukumoto, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73197-7_13 · Cited by 4
Metadata
- Authors
- Kang, D., Ammar, W., Dalvi, B., Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R.
- Affiliations
- Carnegie Mellon University (US)
- Allen Institute for Artificial Intelligence (US)
- University of Washington (US)
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 1647-1661
- OpenAlex
- W2964211782
- Semantic Scholar
- 53489041a08ef1a6cd19b4c95ce092a148283b6b
- Cited by
- 270
- References
- 22
- Citations by year
- 2026: 52 · 2025: 40 · 2024: 27 · 2023: 43 · 2022: 28 · 2021: 32 · 2020: 27 · 2019: 20 · 2018: 1
- FWCI
- 7.10
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- peer review, scholarly publishing, natural language processing applications, research evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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