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2021 SEMEVAL SemEval 2021

SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection

Abstract

AbstractThis paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified and generated by two language models. For the toxic word classification, the prediction threshold value was optimized separately for every comment, in order to maximize the expected F1 value.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing
🐝 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

Michał Satława , Katarzyna Zamłyńska , Jarosław Piersa , Joanna Kolis , Klaudia Firląg , Katarzyna Beksa , Zuzanna Bordzicka , Christian Goltz , Paweł Bujnowski , Piotr Andruszkiewicz

Topics

Artificial Intelligence > Core AI > Foundation Models Machine Learning > Learning Types > Self-Supervised Learning Machine Learning > Application Areas > Data Augmentation Machine Learning > Application Areas > Model Merging Natural Language Processing > Applications > Text Classification Natural Language Processing > Resources & Methods > Large Language Models

Keywords

binary classification ensemble learning data augmentation bert model language model ensemble model pre-trained language model threshold optimization toxic span detection toxic word classification training data generation transformer model binary word classification
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