2020
COLING
COLING 2020
Duluth at SemEval-2020 Task 7: Using Surprise as a Key to Unlock Humorous Headlines
Abstract
AbstractWe use pretrained transformer-based language models in SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines. Inspired by the incongruity theory of humor, we use a contrastive approach to capture the surprise in the edited headlines. In the official evaluation, our system gets 0.531 RMSE in Subtask 1, 11th among 49 submissions. In Subtask 2, our system gets 0.632 accuracy, 9th among 32 submissions.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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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
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Learning Types > Contrastive Learning
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Understanding > Sentiment Analysis
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Large Language Models