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
- Editors:
- Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 381–386
- Language:
- URL:
- https://aclanthology.org/D19-5550
- DOI:
- 10.18653/v1/D19-5550
- Cite (ACL):
- Edison Marrese-Taylor, Pablo Loyola, and Yutaka Matsuo. 2019. An Edit-centric Approach for Wikipedia Article Quality Assessment. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 381–386, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- An Edit-centric Approach for Wikipedia Article Quality Assessment (Marrese-Taylor et al., WNUT 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5550.pdf