Toward leveraging Gherkin Controlled Natural Language and Machine Translation for Global Product Information Development
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http://hdl.handle.net/10045/76107
Título: | Toward leveraging Gherkin Controlled Natural Language and Machine Translation for Global Product Information Development |
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Autor/es: | O’Brien, Morgan |
Palabras clave: | Machine Translation |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 2018 |
Editor: | European Association for Machine Translation |
Cita bibliográfica: | O’Brien, Morgan. “Toward leveraging Gherkin Controlled Natural Language and Machine Translation for Global Product Information Development”. In: Pérez-Ortiz, Juan Antonio, et al. (Eds.). Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain, pp. 293-296 |
Resumen: | Machine Translation (MT) already plays an important part in software development process at McAfee where the technology can be leveraged to provide early builds for localization and internationalization testing teams. Behavior Driven Development (BDD) has been growing in usage as a development methodology in McAfee. Within BDD, the Gherkin Controlled Natural Language (CNL) is a syntax and common terminology set that is used to describe the software or business process in a User Story. Given there exists this control on the language to describe User Stories for software features using Gherkin, we seek to use Machine Translation to Globalize it at high accuracy and without Post-Editing and reuse it as Product Information. This enables global product information development to happen as part of the Software Development Life Cycle (SDLC) and at low cost. |
URI: | http://hdl.handle.net/10045/76107 |
ISBN: | 978-84-09-01901-4 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Derechos: | © 2018 Morgan O’Brien, McAfee LLC. |
Revisión científica: | si |
Versión del editor: | http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf |
Aparece en las colecciones: | EAMT2018 - Proceedings |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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EAMT2018-Proceedings_32.pdf | 1,6 MB | Adobe PDF | Abrir Vista previa | |
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