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

• URL: http://arxiv.org/abs/2007.10827v1
• Date: Tue, 21 Jul 2020 14:06:59 GMT
• Title: newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations For Propaganda Classification
• Authors: Paramansh Singh, Siraj Sandhu, Subham Kumar, Ashutosh Modi
• Abstract summary: This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques.
• Score: 2.0491741153610334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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