Machine Translation and the Translation Profession

This topic examines machine translation and translation technology and their effects on how translation is done and on the translation profession.

Definition

The study of automatic translation systems and translation technology and of their impact on translation practice and the profession.

Scope

This topic covers the development of machine translation from rule-based to statistical and neural systems, computer-aided translation tools such as translation memory and terminology management, the practice of post-editing machine output, and the consequences of these technologies for translators' work, productivity, status, and ethics. It treats translation technology descriptively and considers how automation reshapes the profession rather than offering technical instruction.

Core questions

• How have machine translation systems evolved?
• What is post-editing and how does it change the translator's task?
• How do computer-aided translation tools support human translators?
• What are the consequences of automation for the translation profession?

Key theories

Paradigms of machine translation

The succession from rule-based to statistical and then neural machine translation, with neural systems using learned representations to produce more fluent output, as synthesized in Koehn's account of the field.

Post-editing and human-machine workflows

Research, exemplified by Sharon O'Brien's work, on post-editing machine output and on translation-memory tools, analysing productivity, effort, and quality in human-machine translation workflows.

History

Machine translation dates to the 1950s and the Georgetown-IBM experiment, was set back by the 1966 ALPAC report, and revived through statistical methods in the 1990s and 2000s and neural methods in the 2010s. Translation memory and computer-aided translation tools transformed professional practice from the 1990s, and post-editing of machine output has become a major mode of work.

Debates

Automation and the future of human translators

Debate centres on whether advancing machine translation will deskill or displace translators or instead reshape their role toward post-editing, specialization, and oversight, with implications for status, pay, and ethics.

Key figures

• Philipp Koehn
• Lynne Bowker
• Sharon O'Brien
• Anthony Pym

Seminal works

• bowker2002
• koehn2020
• obrien2011

Frequently asked questions

What is post-editing?

Post-editing is the human revision of raw machine-translation output to bring it to a required quality level, now a common professional task alongside translation from scratch.

Will machine translation replace human translators?

Current evidence and scholarship suggest machine translation reshapes rather than wholly replaces the profession, shifting much work toward post-editing, specialization, and quality oversight, especially for high-stakes and creative texts.