CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection
TL
TL;DR
This paper uses a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask, built using embeddings from BERT in combination with additional lexical features and extensive label post-processing.
Abstract
This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).
References23 items
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SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
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Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
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Fine-Grained Analysis of Propaganda in News Article
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Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
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