Recently, the character-word lattice structure has been proved to be
effective for Chinese named entity recognition (NER) by incorporating the word
information. However, since the lattice structure is complex and dynamic, most
existing lattice-based models are hard to fully utilize the parallel
computation of GPUs and usually have a low inference-speed. In this paper, we
propose FLAT: Flat-LAttice Transformer for Chinese NER, which converts the
lattice structure into a flat structure consisting of spans. Each span
corresponds to a character or latent word and its position in the original
lattice. With the power of Transformer and well-designed position encoding,
FLAT can fully leverage the lattice information and has an excellent
parallelization ability. Experiments on four datasets show FLAT outperforms
other lexicon-based models in performance and efficiency.

本研究提出 FLAT：Flat-LAttice Transformer，将复杂动态的中文命名实体识别字符 - 词格结构转换成跨度组成的平面结构，借助 Transformer 和精心设计的位置编码完全利用格子信息，具有出色的并行化能力和性能效率。在四个数据集上的实验证明，FLAT 在性能和效率方面均优于其他基于词典的模型。