Abstract
The lack of spelling conventions and the natural evolution of human language create a linguistic barrier inherent in historical documents. This barrier has always been a concern for scholars in humanities. In order to tackle this problem, spelling normalization aims to adapt a document’s orthography to modern standards. In this work, we evaluate several character-based neural machine translation normalization approaches—using modern documents to enrich the neural models. We evaluated these approaches on several datasets from different languages and time periods, reaching the conclusion that each approach is better suited for a different set of documents.
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Acknowledgments
The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valenciana (2014–2020) under project IDIFEDER/2018/025; from Ministerio de Economía y Competitividad under project PGC2018-096212-B-C31; and from Generalitat Valenciana (GVA) under project PROMETEO/2019/121.We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research.
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Domingo, M., Casacuberta, F. (2021). A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_24
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