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An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition

机译：中文命名实体识别中多领域多任务学习的实证研究

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摘要

Named entity recognition (NER) often suffers from lack of annotation data. Multi-domain and multi-task learning solve this problem in some degree. However, previous multi-domain and multi-task learning are often studied in English. In the other part, multi-domain and multi-task learning are often researched independently. In this manuscript, we first summarize the previous works of multi-domain and multi-task learning in NER. Then, we introduce the multi-domain and multi-task learning in Chinese NER. Finally, we explore the universal models between multi-domain and multi-task learning. Experiments show that the universal models can be used in Chinese NER and outperform the baseline model.
机译：命名实体识别（NER）通常缺少注释数据。多领域多任务学习在某种程度上解决了这个问题。但是，以前的多领域和多任务学习通常是用英语学习的。另一方面，多领域和多任务学习通常是独立研究的。在本手稿中，我们首先总结了NER中多域和多任务学习的先前工作。然后，我们介绍了中文NER中的多领域和多任务学习。最后，我们探索了多领域和多任务学习之间的通用模型。实验表明，该通用模型可用于中文NER并优于基线模型。

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