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Abstract
Machine translation (MT) is the use of computer software to translate text or speech from one natural language into another. It is a sub-field of computational linguistics that started its development in the 1950s with the aim of breaking down linguistic and cultural obstacles to human communication and understanding. MT is enormously applied in commerce, education, entertainment, government, healthcare, law, and science. However, the challenge is not that simple in light of the fact that languages are highly complex and diverse means of encoding meaning and structure and the context in which they function. More importantly, like human languages, languages change and evolve with their speakers’ social and historical contexts. Thus, MT also faces many challenges and limitations which are sophisticated and adaptive as well.
This chapter reviews the main types of MT that have been proposed and developed over time: rule-based, statistical, hybrid, and neural MT. Its advantages and disadvantages, performance and quality in different scenarios, will also be considered. This overview will describe present trends and developments in MT research and development-including multilingualism, domain adaptation, post-editing, evaluation, and ethics. The main objectives of this chapter are to present an overview of the state-of-the-art in MT, identify key challenges and issues that MT faces today and tomorrow, and outline some possible ways out of them. The scope of the paper is text-based MT only, speech-to-speech or speech-to-text translation excluded. The chapter is written for readers who are interested or have background knowledge in linguistics, computer science, or translation studies.
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