@inproceedings{be56fba55f3c4bdcae95059ab46318a6,
title = "syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection",
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.",
author = "Jinfen Li and Lu Xiao",
note = "Publisher Copyright: {\textcopyright} 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.; 14th International Workshops on Semantic Evaluation, SemEval 2020 ; Conference date: 12-12-2020 Through 13-12-2020",
year = "2020",
language = "English (US)",
series = "14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings",
publisher = "International Committee for Computational Linguistics",
pages = "1808--1816",
editor = "Aurelie Herbelot and Xiaodan Zhu and Alexis Palmer and Nathan Schneider and Jonathan May and Ekaterina Shutova",
booktitle = "14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings",
}