English

newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification

Computation and Language 2020-07-23 v1 Machine Learning

Abstract

This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.

Keywords

Cite

@article{arxiv.2007.10827,
  title  = {newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification},
  author = {Paramansh Singh and Siraj Sandhu and Subham Kumar and Ashutosh Modi},
  journal= {arXiv preprint arXiv:2007.10827},
  year   = {2020}
}

Comments

7 pages, 4 figures, 2 tables Accepted at Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)

R2 v1 2026-06-23T17:16:56.716Z