  We propose the neural string edit distance model for string-pair
classification and sequence generation based on learned 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 test the method
on cognate detection, transliteration, and grapheme-to-phoneme conversion. We
show that we can trade off between performance and interpretability in a single
framework. Using contextual representations, which are difficult to interpret,
we can match the performance of state-of-the-art string-pair classification
models. Using static embeddings and a minor modification of the loss function,
we can force interpretability, at the expense of an accuracy drop.
