Abstract
The goal of fine-grained propaganda detection is to determine whether a given sentence uses propaganda techniques (sentence-level) or to recognize which techniques are used (fragment-level). This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task. In order to better utilize the document information, we construct context-dependent input pairs (sentence-title pair and sentence- context pair) to fine-tune the pretrained BERT, and we also use the undersampling method to tackle the problem of imbalanced data.- Anthology ID:
- D19-5010
- Volume:
- Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 83–86
- Language:
- URL:
- https://aclanthology.org/D19-5010
- DOI:
- 10.18653/v1/D19-5010
- Cite (ACL):
- Wenjun Hou and Ying Chen. 2019. CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 83–86, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection (Hou & Chen, NLP4IF 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-5010.pdf