Porous Lattice-based Transformer Encoder for Chinese NER

Understand

Incorporating lattices into character-level Chinese named entity recognition is an effective method to exploit explicit word information.

• Recent works extend recurrent and convolutional neural networks to model lattice inputs.
• However, due to the DAG structure or the variable-sized potential word set for lattice inputs, these models prevent the convenient use of batched computation, resulting in serious inefficient.
• In this paper, we propose a porous lattice-based transformer encoder for Chinese named entity recognition, which is capable to better exploit the GPU parallelism and batch the computation owing to the mask mechanism in transformer.