@inproceedings{libovicky-fraser-2022-neural,
title = "Neural String Edit Distance",
author = "Libovick{\'y}, Jind{\v{r}}ich and
Fraser, Alexander",
editor = "Vlachos, Andreas and
Agrawal, Priyanka and
Martins, Andr{\'e} and
Lampouras, Gerasimos and
Lyu, Chunchuan",
booktitle = "Proceedings of the Sixth Workshop on Structured Prediction for NLP",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.spnlp-1.6",
doi = "10.18653/v1/2022.spnlp-1.6",
pages = "52--66",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Neural String Edit Distance
%A Libovický, Jindřich
%A Fraser, Alexander
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%Y Lampouras, Gerasimos
%Y Lyu, Chunchuan
%S Proceedings of the Sixth Workshop on Structured Prediction for NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F libovicky-fraser-2022-neural
%X 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.
%R 10.18653/v1/2022.spnlp-1.6
%U https://aclanthology.org/2022.spnlp-1.6
%U https://doi.org/10.18653/v1/2022.spnlp-1.6
%P 52-66
Markdown (Informal)
[Neural String Edit Distance](https://aclanthology.org/2022.spnlp-1.6) (Libovický & Fraser, spnlp 2022)
ACL
- Jindřich Libovický and Alexander Fraser. 2022. Neural String Edit Distance. In Proceedings of the Sixth Workshop on Structured Prediction for NLP, pages 52–66, Dublin, Ireland. Association for Computational Linguistics.