2017
EACL
EACL 2017
Cross-lingual tagger evaluation without test data
Abstract
AbstractWe address the challenge of cross-lingual POS tagger evaluation in absence of manually annotated test data. We put forth and evaluate two dictionary-based metrics. On the tasks of accuracy prediction and system ranking, we reveal that these metrics are reliable enough to approximate test set-based evaluation, and at the same time lean enough to support assessment for truly low-resource languages.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Keyword Pioneer
— cross-lingual evaluation
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Hot Topic Early Bird
— low-resource language
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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
Machine Learning > Learning Types > Zero-Shot Learning
Natural Language Processing > Understanding > Part-of-Speech Tagging
Natural Language Processing > Applications > Text Classification
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Applications > Named Entity Recognition