Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling
Table 2: Performance on cross-domain data  The best score (except the Gold) in each column that is significantly (p<0.05) better than the second best is marked bold, while those are better but not significantly are underlined.
['Task & Corpus Target Domain', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM academic', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM bio', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM fiction', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM news', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM voyage', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM wiki', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM interview', '[BOLD] Multi-Domain POS Tagging: Universal Dependencies v2.3 - GUM AVG  [ITALIC] Acc(%)']
The results of each task on each domain are shown in Tab. We can see that ConNet performs the best on most of the domains and achieves the highest average score for all tasks. We report the accuracy for POS tagging and classification, and the chunk-level F1 score for NER. We can see that ConNet achieves the highest average score on all tasks. MTL-MVT is similar to our decoupling phase and performs much worse. Naively doing unweighted voting does not work well.