Visual Agreement Regularized Training for Multi-Modal Machine Translation
Table 2: The experimental results of Transformer-based systems. The best performance is highlighted in bold.
['Methods', 'EN→ DE BLEU', 'EN→ DE METEOR', 'DE→EN BLEU', 'DE→EN METEOR', 'EN→FR BLEU', 'EN→FR METEOR', 'FR→EN BLEU', 'FR→EN METEOR']
This demonstrates that our approaches are universal, which can bring about consistent improvements in performance on different base neural architectures. However, it is worth noting that although soft regularization can achieve better performance than all baselines in most translation directions, its performance is not comparable to hard regularization. This leads to larger error when leveraging attention distribution to approximate the true alignment distribution, and the overall performance would be correspondingly inferior.