08/12/2020

Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning

Weipeng Huang, Xingyi Cheng, Kunlong Chen, Taifeng Wang, Wei Chu

Keywords:

Abstract Paper Similar Papers

Abstract: The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segmenter to exploit diverse criteria of various datasets. Our model is based on Bidirectional Encoder Representations from Transformers (BERT), which is responsible for introducing open-domain knowledge. Private and shared projection layers are proposed to capture domain-specific knowledge and common knowledge, respectively. We also optimize computational efficiency via distillation, quantization, and compiler optimization. Experiments show that our segmenter outperforms the previous state of the art (SOTA) models on 10 CWS datasets with superior efficiency.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6240-towards-fast-and-accurate-neural-chinese-word-segmentation-with-multi-criteria-learning
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at COLING 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 10:17 
 0
 0
 0
 0
 11:47 
 0
 0
 0
 0
 3:04 
 0
 0
 0
 0
 15:08 
 0
 0
 0
 0
 14:15 
 0
 0
 0
 0
 5:16 
 0
 0
 0
 0
 10:16 
 0
 0
 0
 0
 13:15 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 19:42 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 20:10 
 0
 0
 0
 0
 11:16 
 0
 0
 0
 0
 14:17 
 0
 0
 0
 0
 12:04 
 0
 0
 0
 0
 15:30 
 0
 0
 0
 0
 5:02 
 0
 0
 0
 0
 9:08 
 0
 0
 0
 0
 11:09 
 0
 0
 0
 0
 16:31 
 0
 0
 0
 0
 9:08 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 9:02 
 0
 0
 0
 0
 8:27 
 0
 0
 0
 0
 4:55 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 7:13 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 14:48 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 9:28 
 0
 0
 0
 0
 2:56 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 15:00 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 15:33 
 0
 0
 0
 0
 11:09