1. generating a quality label does not necessarily mean that the model has the ability to predict it. I am wondering if there is some disturbances are made to the sentence in the training data, will the proposed model generate the correct quality label (showing the quality goes down)?
2. according to fig.1 , the prediction of quality labels is not good at all. The model seems not to be able to discriminate candidates with different qualities.
3. using QE label as the generation labels seems to be an interesting idea. Will you please give some examples of the same source sentence translated with different QE labels? It would be nice to see the effect demonstrated.
4. I am not quite sure how is the quality difference between two translations with 1 point difference in MetricX or Comet score. It will be better to give some examples to show how the translation quality is improved indeed.
1. generating a quality label does not necessarily mean that the model has the ability to predict it. I am wondering if there is some disturbances are made to the sentence in the training data, will the proposed model generate the correct quality label (showing the quality goes down)?