Multi-Source Domain Adaptation with Mixture of Experts
Table 4: POS tagging results on SANCL data. Source domains include Web, Emails, Twitter. † indicates the unified multi-source model trained without Twitter, thus can be considered as the oracle performance (upper-bound) of uni-MS.
['Target', 'Non-adversarial best-SS', 'Non-adversarial uni-MS', 'Non-adversarial uni-MS†', 'Non-adversarial MoE', 'adversarial best-SS-A', 'adversarial uni-MS-A', 'adversarial uni-MS-A†', 'adversarial MoE-A']
Again, our approach consistently achieves the best performance across different settings and tasks. Adding Twitter as a source leads to a drop in performance for the unified model, as a result of negative transfer. Our method, however, robustly handles negative transfer and manages to even benefit from this additional source.