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"How can translators protect their jobs from being taken over by machine translation in the future?"
Continuous learning and professional development are crucial for translators to stay ahead of machine translation limitations, as language is constantly evolving.
Machine translation can be helpful for quick, non-vital, low-priority content, but it cannot replace human translators for corporate communication that adheres to a company's particular identity.
Human translators have a human-based approach and can provide greater accuracy and precision, although they can be expensive and time-consuming, just like machine translation.
Despite significant improvements in neural machine translation, it remains dependent on huge sets of training data and may struggle with complex vocabulary and industry-specific terms.
Human translators are invaluable in the field of translation due to their ability to understand and interpret context that machine translation may not fully grasp.
AI won't replace human translators yet, as human translation doesn't just set the standard, it necessarily is the standard.
Big data doesn't have a big sense of humour, and translators are needed to translate jokes, puns, and sly innuendo, as well as nuanced cultural references.
In 2019, a study tested machine translation in an academic medical setting and found that Google Translate got 92% of translations incorrect.
A chi-square test revealed no statistically significant difference between machine translations and human translations, indicating that human translators can still provide better accuracy.
Machine Translation actively tries to guess the possible translation for a source text by using past translations and various natural language processing techniques.
These technologies are complementary to one another, and together they bolster a translator's ability to work faster and improve productivity.
Jost Zetzsche, a translation industry and translation technology consultant, notes that machine translation engines are expected to improve in the near future.
One area where machine translation engines are expected to improve is the edit score, which represents the percentage of the workload that a human translator faces when post-editing a machine-translated text.
According to calculations, the edit score is now around 20-40%.
Neural machine translation still struggles with complex vocabulary and industry-specific terms, which is where human translators can step in.
Machine translation may struggle to convey the correct meaning, often with humorous or disastrous results.
In the 1950s, researchers were convinced that machine translation was almost ready, but it still requires human input to achieve high-quality translations.
Human translators are needed to provide context and cultural understanding that machine translation may not fully grasp.
The promise of completely automated translations has been around for a long time, but it still requires human input to achieve high-quality translations.
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