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.- Anthology ID:
- 2020.semeval-1.231
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1764–1770
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.231
- DOI:
- 10.18653/v1/2020.semeval-1.231
- Cite (ACL):
- Paramansh Singh, Siraj Sandhu, Subham Kumar, and Ashutosh Modi. 2020. newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1764–1770, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- newsSweeper at SemEval-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification (Singh et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.semeval-1.231.pdf
- Code
- paramansh/propaganda_detection