2017
EMNLP
EMNLP 2017
Cross-lingual Character-Level Neural Morphological Tagging
Abstract
AbstractEven for common NLP tasks, sufficient supervision is not available in many languages – morphological tagging is no exception. In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
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Hot Topic Early Bird
- low-resource language
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Natural Language Processing > Understanding > Part-of-Speech Tagging
Natural Language Processing > Resources & Methods > Multilingual NLP
Machine Learning > Learning Types > Transfer Learning
Natural Language Processing > Understanding > Morphology
Artificial Intelligence > Core AI > Natural Language Processing