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Porous Lattice-based Transformer Encoder for Chinese NER | | Xue Mengge
; Yu Bowen
; Liu Tingwen
; Wang Bin
; Meng Erli
; Li Quangang
; | | Date: |
7 Nov 2019 | | Abstract: | 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. We first investigate the
lattice-aware self-attention coupled with relative position representations to
explore effective word information in the lattice structure. Besides, to
strengthen the local dependencies among neighboring tokens, we propose a novel
porous structure during self-attentional computation processing, in which every
two non-neighboring tokens are connected through a shared pivot node.
Experimental results on four datasets show that our model performs up to 9.47
times faster than state-of-the-art models, while is roughly on a par with its
performance. The source code of this paper can be obtained from
this https URL | | Source: | arXiv, 1911.2733 | | Services: | Forum | Review | PDF | Favorites | |
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