@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}