A Computational Approach to Finding Contradictions in User Opinionated Text

Li, Chuqin; Niu, Xi; Al-Doulat, Ahmad; Park, Noseong

doi:10.1109/asonam.2018.8508308

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alamri and Stevenson (2015) use linguistic features and support vector machines (SVMs) to detect contradictions in scientific claims, while Badache et al (2018b) employ review polarity to predict contradiction intensity in reviews. Li et al (2018) combine sentiment analysis with contradiction detection for Amazon reviews. Meanwhile, Lendvai and Reichel (2016) text pairs into entailment, neutral, or contradiction (Chen et al, 2017;Gong et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

When Reviewers Lock Horns: Finding Disagreements in Scientific Peer Reviews

Kumar,

Ghosal,

Ekbal

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

To this date, the efficacy of the scientific publishing enterprise fundamentally rests on the strength of the peer review process. The journal editor or the conference chair primarily relies on the expert reviewers' assessment, identify points of agreement and disagreement and try to reach a consensus to make a fair and informed decision on whether to accept or reject a paper. However, with the escalating number of submissions requiring review, especially in toptier Artificial Intelligence (AI) conferences, the editor/chair, among many other works, invests a significant, sometimes stressful effort to mitigate reviewer disagreements. Here in this work, we introduce a novel task of automatically identifying contradictions among reviewers on a given article. To this end, we introduce Con-traSciView, a comprehensive review-pair contradiction dataset on around 8.5k papers (with around 28k review pairs containing nearly 50k review pair comments) from the open reviewbased ICLR and NeurIPS conferences. We further propose a baseline model that detects contradictory statements from the review pairs. To the best of our knowledge, we make the first attempt to identify disagreements among peer reviewers automatically. We make our dataset and code public for further investigations 1 .

show abstract