Soumya Sarkar


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NwQM: A neural quality assessment framework for Wikipedia
Bhanu Prakash Reddy Guda | Sasi Bhushan Seelaboyina | Soumya Sarkar | Animesh Mukherjee
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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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StRE: Self Attentive Edit Quality Prediction in Wikipedia
Soumya Sarkar | Bhanu Prakash Reddy | Sandipan Sikdar | Animesh Mukherjee
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Wikipedia can easily be justified as a behemoth, considering the sheer volume of content that is added or removed every minute to its several projects. This creates an immense scope, in the field of natural language processing toward developing automated tools for content moderation and review. In this paper we propose Self Attentive Revision Encoder (StRE) which leverages orthographic similarity of lexical units toward predicting the quality of new edits. In contrast to existing propositions which primarily employ features like page reputation, editor activity or rule based heuristics, we utilize the textual content of the edits which, we believe contains superior signatures of their quality. More specifically, we deploy deep encoders to generate representations of the edits from its text content, which we then leverage to infer quality. We further contribute a novel dataset containing ∼ 21M revisions across 32K Wikipedia pages and demonstrate that StRE outperforms existing methods by a significant margin – at least 17% and at most 103%. Our pre-trained model achieves such result after retraining on a set as small as 20% of the edits in a wikipage. This, to the best of our knowledge, is also the first attempt towards employing deep language models to the enormous domain of automated content moderation and review in Wikipedia.