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.
TopIntroduction
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 a key element. 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 as, in many cases, it is impossible to meet the demand for translations only with human translators. In addition, MT systems can be integrated with other tools and resources such as Translation Memories—databases which store already translated text for future use—or multilingual terminology databases—also known as glossaries—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 MT and Neural MT.
Key Terms in this Chapter
Machine Translation: The automatic process carried out by a software for translating text from one language to another.
Post-Editing: The revision process for a text that has been previously translated by an MT system.
Translation Memory: A linguistic database that stores previously translated text so it can be reused for generating new translations.
Adaptive Machine Translation: Machine translation systems whose architecture uses user feedback in order learn and to avoid repeating mistakes.
Hybrid Machine Translation: Machine translation systems whose architecture is based on the combination of the rule-based and the corpus-based approaches with the aim to combine the advantages of both approaches.
Neural Machine Translation: Machine translation systems whose architecture is based on artificial neural networks.
Rule-Based Machine Translation: Machine translation systems whose architecture is based on linguistic information retrieved from dictionaries and grammars.
Corpus-Based Machine Translation: Machine translation systems whose architecture is based on the analysis of bilingual or multilingual corpora.