Transfer Learning for Sequence Labeling Using Source Model and Target Data
Table 3: Overall F1 score in recognizing different target NE categories of the test set of the subsequent step
['[BOLD] CONLL 2003', '[BOLD] CONLL 2003 Baseline (EBO)', '[BOLD] CONLL 2003 W/o Adapter (EBO)', '[BOLD] CONLL 2003 W/ Adapter (EBOA)']
The first column in the table identifies different target NE categories. The other three columns present the results of the models without any TL method (Baseline), with the transferred parameters (W/o Adapter), and with the neural adapter (W/ Adapter), respectively. The results indicate a consistent improvement in the F1 score using transferred parameters and especially using the neural adapter. On average, our best TL model with neural adapter gains 1.83 points of improvement in F1 score on CONLL dataset, and 10.30 points on I-CAB, compared to that obtained by the baseline model. It is evident that our proposed methods are able to improve the performance of recognizing NEs in the target data, regardless of dataset or target NE types.