Neural Structural Correspondence Learning for Domain Adataption
Table 1: Sentiment classification accuracy for the Blitzer:07 task (top tables), and for adaptation from the Blitzer’s product review domains to the Blog domain (bottom table). Test-All presents average results across setups. Statistical significance (with the McNemar paired test for labeling disagreements [Gillick:89, Blitzer:06], p<0.05) is denoted with: ∗ (AE-SCL-SR vs. AE-SCL), + (AE-SCL-SR vs. MSDA), ⋄ (AE-SCL-SR vs. MSDA-DAN), ‡ (AE-SCL vs. MSDA) and ⊙ (AE-SCL vs. MSDA-DAN). All the differences between any model and No-DA are statistically significant.
['Mod.|So.→Tar.', '[BOLD] B→E', '[BOLD] D→E', '[BOLD] K→E', '[BOLD] B→K', '[BOLD] D→K', '[BOLD] E→K', '[BOLD] Test-All']
In the \newciteBlitzer:07 task (top tables), AE-SCL-SR is the best performing model in 9 of 12 setups and on a unified test set consisting of the test sets of all 12 setups (the Test-All column). AE-SCL, MSDA and MSDA-DAN perform best in one setup each. On the unified test set, AE-SCL-SR improves over SCL-MI by 3.8% (error reduction (ER) of 14.8%) and over MSDA-DAN by 2% (ER of 8.4%), while AE-SCL improves over SCL-MI and MSDA-DAN by 2.7% (ER of 10.5%) and 0.9% (ER of 3.8%), respectively. MSDA-DAN and MSDA perform very similarly on the unified test set (0.761 and 0.759, respectively) with generally minor differences in the individual setups.