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2019 | OriginalPaper | Chapter

Enriching Character-Based Neural Machine Translation with Modern Documents for Achieving an Orthography Consistency in Historical Documents

Authors : Miguel Domingo, Francisco Casacuberta

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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Abstract

The nature of human language and the lack of a spelling convention make historical documents hard to handle for natural language processing. Spelling normalization tackles this problem by adapting their spelling to modern standards in order to get an orthography consistency. In this work, we compare several character-based machine translation approaches, and propose a method to profit from modern documents to enrich neural machine translation models. We tested our proposal with four different data sets, and observed that the enriched models successfully improved the normalization quality of the neural models. Statistical models, however, yielded a better result.

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Metadata
Title
Enriching Character-Based Neural Machine Translation with Modern Documents for Achieving an Orthography Consistency in Historical Documents
Authors
Miguel Domingo
Francisco Casacuberta
Copyright Year
2019
Book
New Trends in Image Analysis and Processing – ICIAP 2019

Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-30754-7_7

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