Machine translation systems whose architecture is based on artificial neural networks.
Published in Chapter:
Overview of Machine Translation Development
Irene Rivera-Trigueros (Department of Translation and Interpreting, University of Granada, Spain), María-Dolores Olvera-Lobo (Department of Information and Communication Sciences, University of Granada, Spain), and Juncal Gutiérrez-Artacho (Department of Translation and Interpreting, University of Granada, Spain)
Copyright: © 2021
|Pages: 13
DOI: 10.4018/978-1-7998-3479-3.ch060
Abstract
Access to information is increasingly global, which brings with it the growth in a non-English speaking public and, as such, a demand for tools that allow users to access this information. Faced with this scenario, the development of new tools and technologies is essential. This is the case of machine translation (MT), one of the most popular and used tools to overcome linguistic barriers. As a result, linguistic companies are increasingly offering services related to MT. In addition, MT systems can be integrated with other tools and resources such as Translation Memories or multilingual terminology databases in order to improve the efficacy of the translation process. In this chapter an overview of MT development since its early beginnings in the late 1950s until the present day is proposed. The evolution of MT techniques will be reviewed starting from the traditional approaches—statistical and rule-based MT—and finishing with the latest trends, which include hybrid MT systems, adaptive and neural MT.