WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia


  • Kenichiro Ando RIKEN AIP
  • Satoshi Sekine RIKEN AIP
  • Mamoru Komachi Hitotsubashi University



NLP: Sentence-level Semantics, Textual Inference, etc., PEAI: Bias, Fairness & Equity, ML: Ethics, Bias, and Fairness, PEAI: Safety, Robustness & Trustworthiness


Wikipedia can be edited by anyone and thus contains various quality sentences. Therefore, Wikipedia includes some poor-quality edits, which are often marked up by other editors. While editors' reviews enhance the credibility of Wikipedia, it is hard to check all edited text. Assisting in this process is very important, but a large and comprehensive dataset for studying it does not currently exist. Here, we propose WikiSQE, the first large-scale dataset for sentence quality estimation in Wikipedia. Each sentence is extracted from the entire revision history of English Wikipedia, and the target quality labels were carefully investigated and selected. WikiSQE has about 3.4 M sentences with 153 quality labels. In the experiment with automatic classification using competitive machine learning models, sentences that had problems with citation, syntax/semantics, or propositions were found to be more difficult to detect. In addition, by performing human annotation, we found that the model we developed performed better than the crowdsourced workers. WikiSQE is expected to be a valuable resource for other tasks in NLP.



How to Cite

Ando, K., Sekine, S., & Komachi, M. (2024). WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17656-17663.



AAAI Technical Track on Natural Language Processing I