Improving Captioning for Low-Resource Languages by Cycle Consistency
Table 1: Experimental results on common metrics.
['Model', 'CIDEr', 'BLEU4', 'METEOR']
Firstly, we observe that Cycle-Attn outperforms both Soft-Attn and Trans in most metrics. Particularly, Cycle-Attn outperforms Soft-Attn by +3.32 (8.60%) CIDEr, and +0.55 (10.74%) BLEU4. This observation demonstrates that the proposed architecture can improve low-resource language captioning of either translation-based or alignment-based approach by combining their merits. Secondly, Cycle-Attn achieves better performance on all metrics comparing with Dual-Attn. For example, it improves CIDEr by +1.34 (3.30%) and BLEU4 by +0.35 (6.58%), which validates the effectiveness of the cycle consistency. Finally, by pre-training the English caption model on Flickr30K instead of Multi30K-Trans, Cycle-Attn+ outperforms Cycle-Attn on all metrics. This demonstrates the notable benefit from rich-resource datasets for the proposed architecture. Moreover, Cycle-Attn+ also performs better than Dual-Attn+ on all metrics. This indicates that our approach can benefit from the cycle consistency and rich-resource dataset simultaneously.