2022
EMNLP
EMNLP 2022
HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models
Abstract
AbstractThis paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— diachronic language model
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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
Machine Learning > Learning Types > Self-Supervised Learning
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Large Language Models
Artificial Intelligence > Core AI > Language
Deep Learning > Learning Types > Transfer Learning