| Challenge: | Existing methods to detect information overridden by the Web are not accurate and require a user's perspective to make the decision. |
| Approach: | They propose a task to detect whether a reference sentence has overridden a target sentence by using sentence pairs from the difference between two versions of Wikipedia. |
| Outcome: | The proposed task is formalized as a binary classification problem to determine whether a reference sentence has overridden a target sentence. |
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| Challenge: | Large Language Models (LLMs) are pre-trained on massive data corpora, and the quality of these corporales is one of the main factors in achieving stateof-the-art performance. |
| Approach: | They propose to use Wikipedia-style reformatting to alter a model's quality assessment and enable low-quality content to surpass filtering thresholds. |
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Detecting Sockpuppetry on Wikipedia Using Meta-Learning (2025.acl-long)
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| Challenge: | Existing approaches to model author-specific sockpuppet detection on Wikipedia are limited in data-scarce settings. |
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Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)
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| Challenge: | Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
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It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons (2024.eacl-long)
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| Challenge: | a new model to score subjective bias in documents is developed to perform pairwise comparisons . a recent study shows that the model can be explained and validated for other domains based on the training data. |
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WIKIR: A Python Toolkit for Building a Large-scale Wikipedia-based English Information Retrieval Dataset (2020.lrec-1)
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| Challenge: | ad-hoc information retrieval methods usually require large amounts of annotated data to be effective. |
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Leveraging Wikipedia article evolution for promotional tone detection (2022.acl-long)
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| Challenge: | Detecting biased language is useful for a variety of applications, authors say . a dataset for document-level promotional tone detection is available for WikiEvolve . |
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DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
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A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
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How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP (2026.acl-long)
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Kushal Tatariya, Artur Kulmizev, Wessel Poelman, Esther Ploeger, Marcel Bollmann, Johannes Bjerva, Jiaming Luo, Heather Lent, Miryam de Lhoneux
| Challenge: | Wikipedia’s perceived high quality and broad language coverage have established it as a fundamental resource in NLP. |
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WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection (2020.lrec-1)
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| Challenge: | Existing methods for moderation of abusive content are limited by the lack of large corpora of conversations. |
| Approach: | They propose a framework with comment-level abuse annotations based on the Wikipedia Comment corpus . they propose 'context-based' approaches to detect abusive content based upon conversational context . |
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