---
title: "Evaluating Data Augmentation Strategies for Training Spanish"
description: "Discriminative models for spotting spelling errors need training data with authentic mistakes, and such data is scarce. 14 references."
authors: ["Manuel Castillo-Sancho", "Jordi Porta", "Asunción Gómez-Pérez"]
year: 2026
venue: "Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026"
source: "https://aclanthology.org/2026.cawl-1.7/"
doi: "10.63317/3mw3y4ovzwsz"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sancho-2026-evaluating-data-augmentation-strategies-training/"
---
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# Evaluating Data Augmentation Strategies for Training Spanish Misspelling Detection Models

**Authors:** Manuel Castillo-Sancho, [Jordi Porta](https://md.research.elamur.ai/free/en/authors/jordi-porta/) and Asunción Gómez-Pérez · **Year:** 2026 · **Venue:** [Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026](https://md.research.elamur.ai/free/en/venues/cawl/) ([Workshop on Computation and Written Language @ LREC 2026](https://md.research.elamur.ai/free/en/venues/cawl/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.cawl-1.7/) · [PDF](https://aclanthology.org/2026.cawl-1.7.pdf) · [DOI 10.63317/3mw3y4ovzwsz](https://doi.org/10.63317/3mw3y4ovzwsz) · [OpenAlex](https://openalex.org/W7166551903)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Discriminative models for spotting spelling errors need training data with authentic mistakes, and such data is scarce. This paper asks which kind of synthetic error injection best supports Spanish misspelling detection. The authors use CORRSIC, a linguist-annotated corpus of real errors drawn from CREA. They split it 60/40: the larger part supplies error statistics and the smaller part serves as the test set. Three generators are compared:
- a random character-perturbation model;
- a keyboard model built on a full Spanish layout, including its Shift, AltGr and diacritic layers;
- a statistical model whose edit patterns come from Damerau-Levenshtein alignments and are encoded as a weighted finite-state transducer.

The authors also vary how target words are chosen (randomly or by length) and test two augmentation configurations. The statistical generator comes closest to real error distributions. In detection experiments, scores rise as training data grows, while the word-selection choice matters little. The study gives empirical support for grounding synthetic errors in real data when building spell-checkers.

## Key points
- CORRSIC contains 49,676 text segments, 1,696,344 words and 49,958 annotated misspellings. That is a density of 2.95%, covering 30,008 distinct misspelling types.
- The statistical model achieves the lowest Jensen–Shannon divergence from real errors, at 0.148 nats.
- The WFST operates over the tropical semiring, with edits weighted by negative log probability. Misspellings are sampled from the k = 1000 shortest paths.
- The keyboard model handles only substitutions, with probability decaying exponentially as key distance grows. It does not model insertions or deletions.
- The configurations AUG1 and AUG2 show complementary precision/recall trade-offs. A classifier identifying which generator produced each error reached diagonal shares of about 0.77 (random), 0.85 (keyboard) and 0.79 (statistical).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

Also on the byline: [Real Academia Española](https://ror.org/05t8ffp36).

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## Cite

Cite as: Manuel Castillo-Sancho, Jordi Porta and Asunción Gómez-Pérez. 2026. Evaluating Data Augmentation Strategies for Training Spanish Misspelling Detection Models. Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{castillosancho2026evaluating,
  title = {{Evaluating Data Augmentation Strategies for Training Spanish Misspelling Detection Models}},
  author = {Manuel Castillo-Sancho, Jordi Porta and Asunción Gómez-Pérez},
  booktitle = {Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026},
  year = {2026},
  doi = {10.63317/3mw3y4ovzwsz},
  url = {https://doi.org/10.63317/3mw3y4ovzwsz}
}
```

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## References

14 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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13. Wenqian Ye, Luyang Jiang, Eric Xie, Guangtao Zheng, Yunsheng Ma, Xu Cao, Dongliang Guo, Daiqing Qi, Zeyu He, Yijun Tian, Megan Coffee, Zhe Zeng, Sheng Li, Ting-hao, Huang, Ziran Wang, James M. Rehg, Henry Kautz, and Aidong Zhang. 2025. The clever hans mirage: A comprehensive survey on spurious corre… [find](https://scholar.google.com/scholar?q=Wenqian+Ye%2C+Luyang+Jiang%2C+Eric+Xie%2C+Guangtao+Zheng%2C+Yunsheng+Ma%2C+Xu+Cao%2C+Dongliang+Guo%2C+Daiqing+Qi%2C+Zeyu+He%2C+Yijun+Tian%2C+Megan+Coffee%2C+Zhe+Zeng%2C+Sheng+Li%2C+Ting%2Dhao%2C+Huang%2C+Ziran+Wang%2C+James+M%2E+Rehg%2C+H)
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*Updated 1 November 2026*