2022
COLING
COLING 2022
One Word, Two Sides: Traces of Stance in Contextualized Word Representations
Abstract
AbstractThe way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary 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, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Machine Learning > Core Methods > Representation Learning
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Resources & Methods > Text Representation
Interdisciplinary > Linguistics > Semantics
Machine Learning > Learning Types > Representation Learning