My Conversations with ChatGPT
Machine Translation (MT) and translations generated by AI have brought about a paradigm shift in the translation industry. The output of MT will continue to improve, especially when associated with clients’ translation memories and human editing. Language Service Providers are adapting and are including MT in their processes, when MT is applicable. While linguists will continue to be essential in the process, their role will shift from that of translators to validators of content generated by MT. At Excel Translations, we wanted to know what AI thinks about all of this and decided to have an open conversation on the matter with ChatGPT. I will regularly publish a question and ChatGPT’s response, uncensored.
QUESTION 149:
How many human edits should a machine translation ideally undergo?
Chat GPT’s Response:
The number of human edits required for machine translation (MT) ideally depends on the quality of the initial MT output, the complexity of the text, and the intended use of the translation. However, here are some general guidelines:
Typical Editing Stages:
Light Post-Editing:
Purpose: Suitable for internal documents or content where perfect accuracy is not critical.
Edits Required: Minimal, focusing on correcting obvious errors, ensuring basic readability, and fixing major inaccuracies.
Number of Edits: Relatively few edits per sentence, mainly for grammar, syntax, and major mistranslations.
Full Post-Editing:
Purpose: Required for content where high accuracy and readability are essential, such as marketing materials, official documents, and customer-facing content.
Edits Required: Extensive, ensuring the translation is accurate, contextually appropriate, stylistically consistent, and free of any errors.
Number of Edits: Potentially several edits per sentence, including rephrasing, correcting idiomatic expressions, and ensuring cultural appropriateness.
Summary
The ideal number of human edits for machine translation varies based on the quality of the initial output, the complexity of the text, and its intended use. For high-quality MT outputs, light post-editing with minimal edits may suffice, while more complex or important texts may require full post-editing with several edits per sentence. Using best practices such as iterative and collaborative editing can help achieve the desired translation quality efficiently.
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