- The unsupervised translation tasks are all quite superficial, taking existing datasets of similar languages (e.g. En-De Multi30k, En-Fr WMT) and editing them to an unsupervised MT corpus.
- Improvements on Multi30k are quite small (< 1 BLEU) and reported over single runs and measuring BLEU scores alone. It would be good to report averages over multiple runs and report some more modern metrics as well like COMET or BLEURT.
- It is initially quite unclear from the writing where the sentence-level representations come from. As they are explicitly modeled, they need supervision from somewhere. The constant comparison to latent variable models and calling these sentence representations latent codes does not add to the clarity of the paper. I hope this will be improved in a revision of the paper.
Some typos: - 001: "The latent variables" -> "Latent variables" - 154: "efficiently to compute" -> "efficient to compute" - 299: "We denote the encoder and decoder for encoding and generating source-language sentences as the source encoder and decoder" - unclear - 403: "langauge" -> "language"
- Improvements on Multi30k are quite small (< 1 BLEU) and reported over single runs and measuring BLEU scores alone. It would be good to report averages over multiple runs and report some more modern metrics as well like COMET or BLEURT.