@inproceedings{li-xiao-2020-syrapropa,
title = "syrapropa at {S}em{E}val-2020 Task 11: {BERT}-based Models Design for Propagandistic Technique and Span Detection",
author = "Li, Jinfen and
Xiao, Lu",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.237",
doi = "10.18653/v1/2020.semeval-1.237",
pages = "1808--1816",
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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<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.</abstract>
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%0 Conference Proceedings
%T syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection
%A Li, Jinfen
%A Xiao, Lu
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F li-xiao-2020-syrapropa
%X 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.
%R 10.18653/v1/2020.semeval-1.237
%U https://aclanthology.org/2020.semeval-1.237
%U https://doi.org/10.18653/v1/2020.semeval-1.237
%P 1808-1816
Markdown (Informal)
[syrapropa at SemEval-2020 Task 11: BERT-based Models Design for Propagandistic Technique and Span Detection](https://aclanthology.org/2020.semeval-1.237) (Li & Xiao, SemEval 2020)
ACL