Abstract
This paper describes the BERT-based models proposed for two subtasks in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. We first build the model for Span Identification (SI) based on SpanBERT, and facilitate the detection by a deeper model and a sentence-level representation. We then develop a hybrid model for the Technique Classification (TC). The hybrid model is composed of three submodels including two BERT models with different training methods, and a feature-based Logistic Regression model. We endeavor to deal with imbalanced dataset by adjusting cost function. We are in the seventh place in SI subtask (0.4711 of F1-measure), and in the third place in TC subtask (0.6783 of F1-measure) on the development set.- Anthology ID:
- 2020.semeval-1.237
- 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:
- 1808–1816
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.237
- DOI:
- 10.18653/v1/2020.semeval-1.237
- Cite (ACL):
- Jinfen Li and Lu Xiao. 2020. syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1808–1816, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection (Li & Xiao, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.semeval-1.237.pdf