Investigating Self-Attention Network for Chinese Word Segmentation
Table 5: Cross domain results
['[BOLD] Model', '[BOLD] ZX', '[BOLD] FR', '[BOLD] DL']
Cross-Domain Results We evaluate our model on the three cross-domain datasets, including ZX, FR and DL. “t” means neural type-supervised method is used to learn POS embedding and domain-specific words is generalized to corresponding tag. In “t_b”, we learn different POS embeddings for different positions in a word. ’s domain adaptation techniques. This may be because ZX, FR and DL are all Chinese novels which contain a large number of noun entities and their wring styles are different from news domain. The result shows that BERT has rarely less effect on cross-domain CWS compared with strong domain adaptation methods. The “L-SAN+CRF+BERT+t” model has 21.15%, 25.96% and 1.54% error reduction on ZX/FR/DL datasets, respectively, which shows that the proposed neural type-supervised method can handle out of vocabulary words more effective. For characters within a word, instead of sharing the same POS embedding of the word, we further distinguish POS embeddings of characters according to their position in a word. The “L-SAN+CRF+BERT+t_b” gives 33.65%, 32.69% and 24.62% on three datasets, respectively. We believe that this is due to more supervision information.