Fine-grained Knowledge Fusion for Sequence Labeling Domain Adaptation
Table 4: Results of baselines and fine-grained knowledge fusion methods on CWS.
['Methods', 'Zhuxian F', 'Zhuxian  [ITALIC] ROOV', '5% Weibo F', '5% Weibo  [ITALIC] ROOV']
Compared with the basicKD method, FGKF behaves better (+1.1% F and +2.8% Roov v.s. basicKD on average), as it takes multi-level relevance discrepancies into account. The sample-q method performs better than the domain-q method, which shows the domain feature is better represented at the sample level, not at the domain level. As for the granularity of α, the performances of αelem is better than αsamp, showing the necessity of modeling element-level relevance. And there isn’t a distinct margin between αelem and αmulti as most of the multi-level domain relevance can be included by the element level. Results of FGKF with warm up indicate that starting from sub-optimal point is better than starting from scratch for the target model.