An Edit-centric Approach for Wikipedia Article Quality Assessment

Edison Marrese-Taylor, Pablo Loyola, Yutaka Matsuo


Abstract
We propose an edit-centric approach to assess Wikipedia article quality as a complementary alternative to current full document-based techniques. Our model consists of a main classifier equipped with an auxiliary generative module which, for a given edit, jointly provides an estimation of its quality and generates a description in natural language. We performed an empirical study to assess the feasibility of the proposed model and its cost-effectiveness in terms of data and quality requirements.
Anthology ID:
D19-5550
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
381–386
Language:
URL:
https://www.aclweb.org/anthology/D19-5550
DOI:
10.18653/v1/D19-5550
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http://aclanthology.lst.uni-saarland.de/D19-5550.pdf
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