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.- Anthology ID:
- 2022.spnlp-1.6
- Volume:
- Proceedings of the Sixth Workshop on Structured Prediction for NLP
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- spnlp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–66
- Language:
- URL:
- https://aclanthology.org/2022.spnlp-1.6
- DOI:
- 10.18653/v1/2022.spnlp-1.6
- Cite (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.
- Cite (Informal):
- Neural String Edit Distance (Libovický & Fraser, spnlp 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.spnlp-1.6.pdf
- Code
- jlibovicky/neural-string-edit-distance