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Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
[article]
2021
arXiv
pre-print
Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performance of NER, due to the requirement of large amounts of training data, deep neural networks can hardly scale out to many languages in an industry setting. To tackle this challenge, cross-lingual NER transfers knowledge from a rich-resource language to languages with low resources through pre-trained multilingual
arXiv:2106.00241v1
fatcat:mvayp27cy5hc5gwefgan3ynr3e