  We propose the neural string edit distance model for string-pair matching and
string transduction based on learnable string edit distance. We modify the
original expectation-maximization learned edit distance algorithm into a
differentiable loss function, allowing us to integrate it into a neural network
providing a contextual representation of the input. We evaluate on cognate
detection, transliteration, and grapheme-to-phoneme conversion, and show that
we can trade off between performance and interpretability in a single
framework. Using contextual representations, which are difficult to interpret,
we match the performance of state-of-the-art string-pair matching models. Using
static embeddings and a slightly different loss function, we force
interpretability, at the expense of an accuracy drop.
