Back Attention Knowledge Transfer for Low-resource Named Entity Recognition
Table 3: Evaluation on OntoNotes 4.0
['input', '[BOLD] Approach', '[BOLD] P(%)', '[BOLD] R(%)', '[BOLD] F1(%)']
Evaluation metric is precision, recall and F1-score. For NotoNotes dataset, gold-standard segmentation is available. Gold Seg and No Seg indicate whether or not to use the word segmentation information. Using the baseline model (i.e., Char+BiLSTM+CRF), the performance on Chinese OntoNotes 4.0 without segmentation is relatively lower. The F1-score of Char+BiLSTM+CRF is 60.25%. Adding BANfirst, BANlast and BANave to the baseline model leads to an increase from 60.25% to 67.15%, 71.99% and 72.76%, respectively. Our best model Char+BiLSTM+CRF+BERT+BANlast yields 81.21% F1-score with no segmentation, which outperforms the previous best approach with no segmentation. Even compared with the state-of-the-art method of using gold segmentation information, our model also has a 4.81% improvement.