  Zero-resource named entity recognition (NER) severely suffers from data
scarcity in a specific domain or language. Most studies on zero-resource NER
transfer knowledge from various data by fine-tuning on different auxiliary
tasks. However, how to properly select training data and fine-tuning tasks is
still an open problem. In this paper, we tackle the problem by transferring
knowledge from three aspects, i.e., domain, language and task, and
strengthening connections among them. Specifically, we propose four practical
guidelines to guide knowledge transfer and task fine-tuning. Based on these
guidelines, we design a target-oriented fine-tuning (TOF) framework to exploit
various data from three aspects in a unified training manner. Experimental
results on six benchmarks show that our method yields consistent improvements
over baselines in both cross-domain and cross-lingual scenarios. Particularly,
we achieve new state-of-the-art performance on five benchmarks.
