Modernizing historical documents: A user Study
Autor: | Domingo-Ballester, Miguel, Casacuberta Nolla, Francisco |
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Rok vydání: | 2020 |
Předmět: |
FOS: Computer and information sciences
Historical documents modernization User study Machine translation Computer science Language barrier 02 engineering and technology Modernization theory computer.software_genre 01 natural sciences Artificial Intelligence 0103 physical sciences CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL 0202 electrical engineering electronic engineering information engineering 010306 general physics Computer Science - Computation and Language Linguistics Cultural heritage Work (electrical) Signal Processing Human evaluation 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Computation and Language (cs.CL) LENGUAJES Y SISTEMAS INFORMATICOS computer Software Historical document |
Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
ISSN: | 0167-8655 |
DOI: | 10.1016/j.patrec.2020.02.027 |
Popis: | [EN] Accessibility to historical documents is mostly limited to scholars. This is due to the language barrier inherent in human language and the linguistic properties of these documents. Given a historical document, modernization aims to generate a new version of it, written in the modern version of the document's language. Its goal is to tackle the language barrier, decreasing the comprehension difficulty and making historical documents accessible to a broader audience. In this work, we proposed a new neural machine translation approach that profits from modern documents to enrich its systems. We tested this approach with both automatic and human evaluation, and conducted a user study. Results showed that modernization is successfully reaching its goal, although it still has room for improvement. The authors wish to thank the anonymous reviewers for their careful reading and in-depth criticisms and suggestions. 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 Sistemas de frabricacion inteligentes para la industria 4.0 (grant agreement IDIFEDER/2018/025); from Ministerio de Economia y Competitividad (MINECO) under project MISMIS-FAKEnHATE (grant agreement PGC2018-096212-B-C31); from Fundacion BBVA under project Carabela (grant agreement CARABELA); and from Generalitat Valenciana (GVA) under project DeepPattern (grant agreement PROMETEO/2019/121). We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research, and Andres Trapiello and Ediciones Destino for granting us permission to use their book in our research. Additionally, we would like to thank all the volunteers that took part in the user study, and the scholars from Prolope that took part in the human evaluation. |
Databáze: | OpenAIRE |
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