Visual Agreement Regularized Training for Multi-Modal Machine Translation
Table 1: The experimental results of Seq2Seq-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']
For instance, our soft regularization model outperforms the text-only Seq2Seq model and the best-performing baseline by 2.2 and 1.1 BLEU score respectively on the EN→DE language pair. This illustrates that visual features are capable of promoting the performance of machine translation, and our approaches can make better use of visual information.