  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.
