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

• URL: http://arxiv.org/abs/2008.10163v1
• Date: Mon, 24 Aug 2020 02:15:29 GMT
• Title: syrapropa at SemEval-2020 Task 11: BERT-based Models Design For Propagandistic Technique and Span Detection
• Authors: Jinfen Li, Lu Xiao
• Abstract summary: 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.
• Score: 2.0051855303186046
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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