syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection

Jinfen Li, Lu Xiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings
EditorsAurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
PublisherInternational Committee for Computational Linguistics
Pages1808-1816
Number of pages9
ISBN (Electronic)9781952148316
StatePublished - 2020
Externally publishedYes
Event14th International Workshops on Semantic Evaluation, SemEval 2020 - Barcelona, Spain
Duration: Dec 12 2020Dec 13 2020

Publication series

Name14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings

Conference

Conference14th International Workshops on Semantic Evaluation, SemEval 2020
Country/TerritorySpain
CityBarcelona
Period12/12/2012/13/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Science Applications

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