1) it uses different experiment settings (e.g., a 32k batch size, a beam size of 5) and does not mention some details (e.g., the number of training steps), these different settings may contribute the performance and the comparisons in Table 2 may not be sufficiently reliable.
2) it only compares with some weak baselines in Tables 3, 4 and 6. Though the approach can surpass the sentence-level model baseline, the naive document-to-document translation model and Zheng et al. (2020), these baselines seem weak, for example, Voita et al. (2019) achieve 81.6, 58.1, 72.2 and 80.0 for deixis, lexical cohesion, ellipsis (infl.) and ellipsis (VP) respectively with the CADec model, while this work only gets 64.7, 46.3, 65.9 and 53.0. It seems that there is still a large gap with the presented approach in these linguistic evaluations.
3) the multi-resolutional data processing approach may somehow increase the instance weight of the document-level data, and how this affects the performance is not studied.
1) It's better to adopt experiment settings consistent with previous work.
2) There is still a large performance gap compared to Voita et al. (2019) in linguistic evaluations, while BLEU may not be able to reflect these document-level phenomena and linguistic evaluations are important. The issues for the performance gap shall be investigated and solved.
3) It's better to investigate whether the model really leverages document-level contexts correctly, probably refer to this paper: Do Context-Aware Translation Models Pay the Right Attention? In ACL 2021.
2) it only compares with some weak baselines in Tables 3, 4 and 6. Though the approach can surpass the sentence-level model baseline, the naive document-to-document translation model and Zheng et al. (2020), these baselines seem weak, for example, Voita et al. (2019) achieve 81.6, 58.1, 72.2 and 80.0 for deixis, lexical cohesion, ellipsis (infl.) and ellipsis (VP) respectively with the CADec model, while this work only gets 64.7, 46.3, 65.9 and 53.0. It seems that there is still a large gap with the presented approach in these linguistic evaluations.