WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia
Cheng Hsu, Cheng-Te Li, Diego Saez-Trumper, Yi-Zhan Hsu

TL;DR
This paper introduces WikiContradiction, a dataset and a neural network model for detecting self-contradictory articles on Wikipedia, improving quality control by reasoning over sentence pairs.
Contribution
It presents the first dataset and model specifically designed for self-contradiction detection in Wikipedia articles, incorporating contradiction-aware comparison and pre-training strategies.
Findings
PCNN achieves promising performance on WikiContradiction dataset.
The model effectively highlights contradiction sentence pairs.
Pre-training on SNLI and MNLI improves detection accuracy.
Abstract
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…
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Taxonomy
TopicsWikis in Education and Collaboration · Topic Modeling · Natural Language Processing Techniques
