Neural String Edit Distance

Jindřich Libovický, Alexander Fraser

SPNLP 2022

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.