Neural String Edit Distance

• URL: http://arxiv.org/abs/2104.08388v1
• Date: Fri, 16 Apr 2021 22:16:47 GMT
• Title: Neural String Edit Distance
• Authors: Jind\v{r}ich Libovick\'y, Alexander Fraser
• Abstract summary: We propose the neural string edit distance model for string-pair classification and sequence generation. We modify the original expectation-maximization learned edit distance algorithm into a differentiable loss function. We show that we can trade off between performance and interpretability in a single framework.
• Score: 77.72325513792981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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