Target-Oriented Fine-tuning for Zero-Resource Named Entity Recognition

Abstract

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.

arXiv comment9 pages, ACL21 Findings

References in corpus (6)

• Word Translation Without Parallel Data
• Towards Lingua Franca Named Entity Recognition with BERT
• Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer
• Coreference Resolution as Query-based Span Prediction
• UniTrans: Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
• Coarse-to-Fine Pre-training for Named Entity Recognition