Neural String Edit Distance. (arXiv:2104.08388v2 [cs.CL] UPDATED)

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 …

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