1. The paper don't compare their models with more recent SOTAs [1-3], so it can not get higher soundness.
2. You should provide the results on more datasets, such as Test2018.
3. You should provide the METEOR results, which is also reported in recent works [1-5].
4. The Figure 5 is not clear, you should give more explanation about it.
[1] Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu Richard Chen, Rogerio S Feris, David Cox, and Nuno Vasconcelos. 2022. VALHALLA: Visual Hallucination for Machine Translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5216–5226.
[2] Junjie Ye, Junjun Guo, Yan Xiang, Kaiwen Tan, and Zhengtao Yu. 2022. Noiserobust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation. In Proceedings of the 29th International Conference on Computational Linguistics. 5098–5108.
[3] Junjie Ye and Junjun Guo. 2022. Dual-level interactive multimodal-mixup encoder for multi-modal neural machine translation. Applied Intelligence 52, 12 (2022), 14194–14203.
[4] Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 6153–6166.
[5] Li B, Lv C, Zhou Z, et al. On Vision Features in Multimodal Machine Translation[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022: 6327-6337.
3. You should provide the METEOR results, which is also reported in recent works [1-5].