Creating the best development corpus for Statistical Machine Translation systems

• Autores: Mara Chinea Rios, Germán Sanchis Trilles, Francisco Casacuberta Nolla
• Localización: Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain / coord. por Juan Antonio Pérez Ortiz , Felipe Sánchez Martínez , Miquel Esplà Gomis, Maja Popovic, Celia Rico Pérez , André Martins, Joachim Van den Bogaert, Mikel L. Forcada Zubizarreta , 2018, ISBN 978-84-09-01901-4, págs. 99-108
• Idioma: inglés
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• Resumen

  • We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged for translating a specific test set, without further data from the domain at hand. Such test set stems from a real application of machine translation, where the texts of a specific e-commerce were to be translated. For developing our development-set selection techniques, we first conducted experiments in a controlled scenario, where labelled data from different domains was available, and evaluated the techniques both with classification and translation quality metrics. Then, the best-performing techniques were evaluated on the e-commerce data at hand, yielding consistent improvements across two language directions.