Graph Optimal Transport for Cross-Domain Alignment
Table 9: Ablation study of the hyper-parameter λ on the EN-VI machine translation dataset.
['[ITALIC] λ', '0', '0.1', '0.3', '0.5', '0.8', '1.0']
S4SS3SSS0Px2 Hyper-parameter When λ=0.8, EN-VI translation performs the best, which indicates that the weight on WD needs to be larger than the weight on GWD, since intuitively node matching is more important than edge matching for machine translation. However, both WD and GWD contribute to GOT achieving the best performance.