Hey, I appreciate the perspective. We definitely should cite both those papers, and will do so in the next version of our draft. There are a lot of papers in this area, and they're all a few years old now, so you might understand how we missed two of them.
We tested all of the methods in the Python Optimal Transport package (https://pythonot.github.io/) and reported the max in most of our tables. So some of this is covered. A lot of these methods also require a seed dictionary, which we don't have in our case. That said, you're welcome to take any number of these tools and plug them into our codebase; the results would definitely be interesting, although we can expect the adversarial methods still work best, as they do in the problem settings you mention.
As for the name – the paper you recommend is called 'vecmap' which seems equally general, doesn't it? Google shows me there are others who have developed their own 'vec2vec'. There is a lot of repetition in AI these days, so collisions happen.
We tested all of the methods in the Python Optimal Transport package (https://pythonot.github.io/) and reported the max in most of our tables. So some of this is covered. A lot of these methods also require a seed dictionary, which we don't have in our case. That said, you're welcome to take any number of these tools and plug them into our codebase; the results would definitely be interesting, although we can expect the adversarial methods still work best, as they do in the problem settings you mention.
As for the name – the paper you recommend is called 'vecmap' which seems equally general, doesn't it? Google shows me there are others who have developed their own 'vec2vec'. There is a lot of repetition in AI these days, so collisions happen.