← Back to papers
2020 COLING COLING 2020

TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles

Abstract

AbstractIn this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Moonsung Kim , Steven Bethard

Topics

Artificial Intelligence > Core AI > Multimodal Learning Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Machine Learning > Application Areas > Model Merging Machine Learning > Learning Types > Transfer Learning

Keywords

transfer learning ensemble learning text classification named entity recognition bert model ensemble method propaganda detection roberta model transformer model
Download PDF

Related papers

Persuasiveness of News Editorials depending on Ideology and Personality 2020
A Graph Representation of Semi-structured Data for Web Question Answering 2020
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations 2020
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism 2020
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network 2020