reviews: 
Comments: This paper develops an approach for unsupervised SL translation and generation entirely using from non-parallel datasets. The motivation is that there is not a lot of paired text and sign language video, so the authors leverage ideas in machine translation and multimodal modeling to build better (unsupervised) sign-text representations. 

The approach contains 3 parts: a masked seq2seq text reconstruction module, signing video reconstruction which uses downsampled discrete latent representations (VQ-VAE) with a GPT-style decoder, and back-translation between each modalities to go from text-to-video-to-text and video-to-text-to-video. There is a disconnect in lengths of text and video sequences, so they use a sliding window aligner to map between each. 

Results are in some cases better than a supervised baseline on the same dataset and show promise for the approach.
Strengths: * Developing unsupervised approaches for SL generation/translation is important, especially given the many different representations used for signing. One could imagine fine-tuning this approach for any given representation (e.g., Glosses, HamNoSys). 
* There are reasonable comparisons to supervised approaches.  
* The ablations /sensitivity analysis comparing this approach with different aspects turned off is interesting. 
* Given the lack of work in this area, it was valuable to see comparisons such as Table 6 on the WMT 2022 sign language translation task
Weaknesses: Overall the results (e.g., Table 1 & 2) are seemingly very poor. This is by no means a reason to reject a paper, but it does in my opinion require the authors to dig deep into 'why' the results are poor and to work towards building an understanding for how they can be improved significantly. It is nice to see that some results are better than the supervised baseline from Albanie et al., but in an absolute sense they are still low. Are there oracle experiments that could be run? How can the problem be made easier to better understand paths towards success?

One thing that immediately stuck out after going through the appendix is that the visual quality of the SL generations, and likely even the video reconstructions, appear to be too low fidelity to capture important hand or face information. Has there been any experimentation around using different resolution inputs for the video model? Perhaps by doubling or quadrupling the video resolution the model would be able to pick up on more nuance. An alternative approach might be to use key point or whole-body representations (e.g., SMPL) as many recent papers on SL translation have done. 

One limitation of the existing approach is that (if I understand correctly) it exclusively trains on BOBSL. On the text encoder side I could imagine it being valuable to leverage existing LLMs and then fine tune. Perhaps the same could be done on the video side? Although I'm not sure what pertaining model or dataset would be the most effective for signings. 

On page 5, there is a reference to Sutton-Spence & Woll stating that when signers are translating text then signs will tend to follow the English word order. While this may be true for translating text, it's unclear if it is correct for the datasets used in this paper. Have you validated this on your datasets?
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 2 fair