Investigating Self-Attention Network for Chinese Word Segmentation
Table 4: In domain results
['[BOLD] Models', '[BOLD] CTB6', '[BOLD] PKU', '[BOLD] MSR']
In-Domain Results We evaluate our model on three news datasets, including CTB, PKU and MSR. Compared with the baseline “BiLSTM+CRF” model, the proposed “L-SAN+CRF” model can achieve similar results, which proves that self-attention network can be a competitive feature extractor for CWS besides recurrent neural network. When replacing word2vec character embedding with BERT, the “BiLSTM+CRF” model gives 41.3%, 30.6% and 31.0% error reduction on CTB6/PKU/MSR, respectively, and the “L-SAN+CRF” model has 41.3%, 32.7% and 41.3% error reductions on three in-domain datasets, respectively. Finally, “L-SAN+CRF” slightly outperforms “BiLSTM+CRF” when using BERT as unigram character representation.