@inproceedings{guda-etal-2020-nwqm,
title = "{N}w{QM}: A neural quality assessment framework for {W}ikipedia",
author = "Guda, Bhanu Prakash Reddy and
Seelaboyina, Sasi Bhushan and
Sarkar, Soumya and
Mukherjee, Animesh",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.674/",
doi = "10.18653/v1/2020.emnlp-main.674",
pages = "8396--8406",
abstract = "Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8{\%} improvement over state-of-the-art approaches with detailed ablation studies."
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<abstract>Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.</abstract>
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%0 Conference Proceedings
%T NwQM: A neural quality assessment framework for Wikipedia
%A Guda, Bhanu Prakash Reddy
%A Seelaboyina, Sasi Bhushan
%A Sarkar, Soumya
%A Mukherjee, Animesh
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F guda-etal-2020-nwqm
%X Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia Quality Monitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.
%R 10.18653/v1/2020.emnlp-main.674
%U https://aclanthology.org/2020.emnlp-main.674/
%U https://doi.org/10.18653/v1/2020.emnlp-main.674
%P 8396-8406
Markdown (Informal)
[NwQM: A neural quality assessment framework for Wikipedia](https://aclanthology.org/2020.emnlp-main.674/) (Guda et al., EMNLP 2020)
ACL
- Bhanu Prakash Reddy Guda, Sasi Bhushan Seelaboyina, Soumya Sarkar, and Animesh Mukherjee. 2020. NwQM: A neural quality assessment framework for Wikipedia. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8396–8406, Online. Association for Computational Linguistics.