2022
COLING
COLING 2022
How about Time? Probing a Multilingual Language Model for Temporal Relations
Abstract
AbstractThis paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages. Results show an advantage of contextualized embeddings over static ones and a detrimen- tal role of sentence level embeddings. While obtaining competitive results against state-of-the-art systems, our probes indicate a lack of suitable encoded information to properly address this task.
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The Questioner
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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, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Types > Representation Learning
Artificial Intelligence > Core AI > Large Language Models
Natural Language Processing > Applications > Natural Language Inference
Natural Language Processing > Resources & Methods > Language Modeling