A Self-training Approach for Few-Shot Named Entity Recognition

Named entity recognition (NER) is a basic task in natural language processing and can be used in a wide range of downstream tasks, such as question answering, text summarization, and machine translation. In recent years, deep-learning based methods achieve great performance in the NER task. It often demands a huge amount of data to train models. However, it is very expensive to collect sufficient training data in many real-world applications. Thus, it is important to develop NER systems for few-shot settings. In this paper, we propose a self-training approach for NER that employs the framework of the machine reading comprehension model when lacking training samples. Experimental results on NER benchmarks demonstrate that the proposed method in this paper outperforms the state-of-the-art methods.