In low-resource natural language processing (NLP), the key problem is a lack of training data in the target language. Cross-lingual methods have had notable success in addressing this concern, but in certain common circumstances, such as insufficient pre-training corpora or languages far from the source language, their performance suffers. In this work we propose an alternative approach to building low-resource Named Entity Recognition (NER) models using "non-speaker" (NS) annotations, provided by annotators with no prior experience in the target language. We recruit 30 participants to annotate unfamiliar languages in a carefully controlled annotation experiment, using Indonesian, Russian, and Hindi as target languages. Our results show that use of non-speaker annotators produces results that approach or match performance of fluent speakers. NS results are also consistently on par or better than cross-lingual methods built on modern contextual representations, and have the potential to further outperform with additional effort. We conclude with observations of common annotation practices and recommendations for maximizing non-speaker annotator performance.

通过使用非母语注释方法，在低资源情况下的自然语言处理中提出一种改进低资源NER模型的方法，通过实验验证该方法的有效性及优越性，可以在一定程度上替代现有的跨语言方法。

利用非说话者注释构建低资源命名实体识别模型