  Named Entity Recognition (NER) aims to extract and classify entity mentions
in the text into pre-defined types (e.g., organization or person name).
Recently, many works have been proposed to shape the NER as a machine reading
comprehension problem (also termed MRC-based NER), in which entity recognition
is achieved by answering the formulated questions related to pre-defined entity
types through MRC, based on the contexts. However, these works ignore the label
dependencies among entity types, which are critical for precisely recognizing
named entities. In this paper, we propose to incorporate the label dependencies
among entity types into a multi-task learning framework for better MRC-based
NER. We decompose MRC-based NER into multiple tasks and use a self-attention
module to capture label dependencies. Comprehensive experiments on both nested
NER and flat NER datasets are conducted to validate the effectiveness of the
proposed Multi-NER. Experimental results show that Multi-NER can achieve better
performance on all datasets.
