2021
ACL
ACL 2021
Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense
Abstract
AbstractThis paper describes our submission to theSemEval’21: Task 7- HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1bwith an RMSE of 0.5339
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— intermediate fine-tuning
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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 > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Application Areas > Data Augmentation
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Large Language Models
Machine Learning > Learning Types > Multi-Task Learning