WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia

Cheng Hsu, Cheng Te Li, Diego Saez-Trumper, Yi Zhan Hsu

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

While Wikipedia has been utilized for fact-checking and claim verification to debunk misinformation and disinformation, it is essential to either improve article quality and rule out noisy articles. Self-contradiction is one of the low-quality article types in Wikipedia. In this work, we propose a task of detecting self-contradiction articles in Wikipedia. Based on the "self-contradictory"template, we create a novel dataset for the self-contradiction detection task. Conventional contradiction detection focuses on comparing pairs of sentences or claims, but self-contradiction detection needs to further reason the semantics of an article and simultaneously learn the contradiction-aware comparison from all pairs of sentences. Therefore, we present the first model, Pairwise Contradiction Neural Network (PCNN), to not only effectively identify self-contradiction articles, but also highlight the most contradiction pairs of contradiction sentences. The main idea of PCNN is two-fold. First, to mitigate the effect of data scarcity on self-contradiction articles, we pre-train the module of pairwise contradiction learning using SNLI and MNLI benchmarks. Second, we select top-K sentence pairs with the highest contradiction probability values and model their correlation to determine whether the corresponding article belongs to self-contradiction. Experiments conducted on the proposed WikiContradiction dataset exhibit that PCNN can generate promising performance and comprehensively highlight the sentence pairs the contradiction locates.

原文English
主出版物標題Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
編輯Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
發行者Institute of Electrical and Electronics Engineers Inc.
頁面427-436
頁數10
ISBN(電子)9781665439022
DOIs
出版狀態Published - 2021
事件2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
持續時間: 2021 12月 152021 12月 18

出版系列

名字Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
國家/地區United States
城市Virtual, Online
期間21-12-1521-12-18

All Science Journal Classification (ASJC) codes

  • 資訊系統與管理
  • 人工智慧
  • 電腦視覺和模式識別
  • 資訊系統

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