AAAI Press Formatting Instructions for Authors Using LaTeX — A Guide
Table 5: Translation results when different supervision signals are used for training our multi-domain model. “Distillation (sequence)” and “Distillation (word)” denote applying distillation at sequence and word level, respectively.
['[BOLD] #', '[BOLD] Model', '[BOLD] BLEU']
As seen, adding supervision signal consistently improves the performance over the “Domain Transformation” model (Rows 6-7), and combining both signals accumulatively achieves the best performance (+1.9 BLEU, Row 8). This confirms the hypothesis in the section of domain supervision that the effects are reflected in three aspects: 1) weak supervision encourages model to exploit both shared and domain-aware knowledge across domains; 2) strong supervision guides model to learn distinct features; 3) combination makes them complementary to each other. It is also interesting to investigate the effect of domain supervision without transformation networks (Rows 1-3), which still improves performance (31.45 vs. 30.90), demonstrating the effectiveness and universality of domain supervision.