Cross-Domain NER using Cross-Domain Language Modeling
Jia Chen, Xiaobo Liang, Yue Zhang · 2019
Due to limitation of labeled resources, crossdomain named entity recognition (NER) has been a challenging task.Most existing work considers a supervised setting, making use of labeled data for both the source and target domains.A disadvantage of such methods is that they cannot train for domains without NER data.To address this issue, we consider using cross-domain LM as a bridge cross-domains for NER domain adaptation, performing crossdomain and cross-task knowledge transfer by designing a novel parameter generation network.Results show that our method can effectively extract domain differences from crossdomain LM contrast, allowing unsupervised domain adaptation while also giving state-ofthe-art results among supervised domain adaptation methods.