Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation
Table 3: Results of domain adaptation on three tasks, where zh denotes the Weibo datasets (in Chinese), and en denotes the Twitter dataset (in English).
['Methods', 'CWS Zhuxian', 'CWS Zhuxian', 'CWS 5% Weibo', 'CWS 5% Weibo', 'POS', 'POS', 'NER', 'NER']
Compared with other methods, FGKF achieves the best results in both F and ROOV. Results demonstrate the effectiveness of our fine-grained knowledge fusion architecture for domain adaptation, and also show the significance of considering sample-level and element-level relevance discrepancies. To further verify the effectiveness of FGKF, we conduct experiments on POS and NER tasks, using F1-score as the evaluation criterion. In these tasks, FGKF achieves better results than other adaptation methods. Extra gain could be obtained by using pre-trained embedding. These results also verify the generalization of our method over different tasks and languages.