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Apr 16, 2021 · We propose the neural string edit distance model for string-pair matching and string transduction based on learnable string edit distance.
Abstract. We propose the neural string edit distance model for string-pair matching and string transduction based on learnable string edit distance.
May 27, 2022 · We reformulate the EM training used to train learnable edit distance as a differentiable loss func- tion that can be used in a neural network.
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This repository contains source code for the paper Neural String Edit Distance. Sequence classification. Classification using neural edit distance: ...
The original expectation-maximization learned edit distance algorithm is modified into a differentiable loss function, allowing it to integrate into a ...
Apr 22, 2021 · The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different ...
We propose a learning-based edit distance embedding method, which improves over prior data-independent approaches.
May 11, 2024 · Given two strings, the minimum edit distance is the lowest number of operations needed to transform one string into the other. It has many ...
Apr 16, 2021 · We propose the neural string edit distance model for string-pair classification and sequence generation based on learned string edit ...
Edit distance is a string metric, ie a way of quantifying how dissimilar two strings (eg, words) are to one another
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