Research of types and current state of machine translation

Abstract

The background of machine translation dates back to the 1950s when scientists began exploring the use of computers for translation. Motivated by various challenges and needs, such as the high cost of manual translation and cross-cultural communication, machine translation has become a pivotal field. This overview delves into the research background, content, methods, key figures, conclusions, and future prospects of machine translation. It summarizes automatic evaluation metrics, corpus construction, and transfer learning, all of which contribute to enhancing translation performance. Currently, there are three mainstream categories of methods, which include rule-based translation, statistical translation, and neural network-based translation. The rule-based translation method relies on language rules and dictionaries for translation. Statistical machine translation involves the use of extensive bilingual corpora for identification and translation. The conclusion emphasizes the potential of neural machine translation, yet acknowledges challenges in diverse languages, low-resource languages, and specialized terminology.