Weakness:
The unbalanced data scenario has not been properly explored by experiments. Under what circumstances can it be counted as an unbalanced data scenario, and what is the data ratio? Therefore, the experiments should not pay more attention to one given setting like TED, WMT, etc., but should construct unbalanced scenarios of different ratios by sampling data in one setting like WMT to verify this important issue.
There is a lack of a reasonable ablation study on the upsampling parameter T, so we cannot confirm whether the oversampling overfit phenomenon will occur, and to what extent will the upsampling reach.
Some baselines are missing in the experimental comparison, such as 1) giving different weights to the loss of unbalanced translation pairs so that in the later stages of training, there will be no situation where rich-resource pairs dominate the training loss; 2) the use of low-resource language pairs further finetune the multilingual model and use the method like R3F to maintain the generalization ability of the model.
In some low-resource language translations from 1.2->2.0, although the improvement of 0.8 can be claimed, it is insignificant in a practical sense.
Missing References:
Aghajanyan, Armen, et al. "Better Fine-Tuning by Reducing Representational Collapse." International Conference on Learning Representations. 2020.
2) the use of low-resource language pairs further finetune the multilingual model and use the method like R3F to maintain the generalization ability of the model. In some low-resource language translations from 1.2->2.0, although the improvement of 0.8 can be claimed, it is insignificant in a practical sense. Missing References: Aghajanyan, Armen, et al. "Better Fine-Tuning by Reducing Representational Collapse." International Conference on Learning Representations. 2020.