Back Attention Knowledge Transfer for Low-resource Named Entity Recognition
Table 4: Evaluation on Weibo NER
['[BOLD] Approach', '[BOLD] NE  [BOLD] P(%)', '[BOLD] NE  [BOLD] R(%)', '[BOLD] NE  [BOLD] F1(%)', '[BOLD] NM  [BOLD] P(%)', '[BOLD] NM  [BOLD] R(%)', '[BOLD] NM  [BOLD] F1(%)', '[BOLD] Overall  [BOLD] P(%)', '[BOLD] Overall  [BOLD] R(%)', '[BOLD] Overall  [BOLD] F1(%)']
Since the dataset is too small, the baseline model (i.e., Char+BiLSTM+CRF) gives 32.35%, 35.31% and 32.18% F1-score on named entities, nominal entities and both without external data and manual features. Using the transferred model knowledge captured by BAN, the model achieves significant improvements. Observations on the performances of different BANs are consistent with that on OntoNotes. The BANave achieves the best performance, which yields 11.33% improvements on average.