Neural String Edit Distance
Jindřich Libovický, Alexander Fraser · 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 expectationmaximization 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.