For the uninitiated:
A siamese neural network consists of twin networks which accept distinct inputs but are joined by an energy function at the top. This function computes some metric between the highest level feature representation on each side. The parameters between the twin networks are tied. Weight tying guarantees that two extremely similar images could not possibly be mapped by their respective networks to very different locations in feature space because each network computes the same function.One Shot
Replace the word images with two representations of the same record across any two tables and you have an algorithm for producing good distance functions for efficient record linkage. Triplet loss is a natural extension to this. Looking forward to seeing some bottom line results comparing it to generic supervised results, which reminds me of the fact that I am unaware of any large benchmark datasets for the fundamental problem of statistical record linkage.